CMPSubset
Updates
Update 2021.08.19
Pursuing Dim25 (85% of variance explained) res1 and res1.5 for biomark targets
Update 2021.08.11
Most thorough way to readdress differences between original and current analyses is to jsut redo the biomark. To that end, will restart analysis of just CMPm2, excluding other datasets (i.e., LSK, MEP, GMP). Will also keep
Notebook setup
Creating new pipeline using seurat v4.0.2 available 2021.06.08
Load libraries required for Seuratv4
Load libraries
<<<<<<< HEADlibrary(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(Seurat)Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObjectlibrary(patchwork)
library(ggplot2)
knitr::opts_knit$set(root.dir = "~/Desktop/10XGenomicsData/msAggr_scRNASeq/CMP/")
# library(clustree)Set global variables
projectName <- "CMP"
jackstraw.dim <- 40library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(clustree)Set global variables
projectName <- "CMP"
jackstraw.dim <- 40Store session info
sessionInfo.filename <- paste0(projectName, "_sessionInfo.txt")
sink(sessionInfo.filename)
sessionInfo()
sink()Load local scripts
source("../RFunctions/read_10XGenomics_data.R")
source("../RFunctions/PercentVariance.R")
source("../RFunctions/Mouse2Human_idconversion.R")
source ("../RFunctions/ColorPalette.R")Read CMPm2
setwd("../../cellRanger/") # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = "CMPm2")
object.data <-Read10X(data_file.list)cmp.object<- CreateSeuratObject(counts = object.data, min.cells = 3, min.genes = 200, project = projectName)Clean up to free memory
remove(object.data)Add mitochondrial metadata and plot some basic features
cmp.object[["percent.mt"]] <- PercentageFeatureSet(cmp.object, pattern = "^mt-")
VlnPlot(cmp.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
plot1 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
Filter data
remove low quality cells require: nFeature_RNA between 200 and 4000 (inclusive) require: percent.mt <=5
print(paste("original object:", nrow(cmp.object@meta.data), "cells", sep = " "))[1] "original object: 12540 cells"cmp.object <- subset(cmp.object,
subset = nFeature_RNA >=200 &
nFeature_RNA <= 4000 &
percent.mt <= 5
)
print(paste("new object:", nrow(cmp.object@meta.data), "cells", sep = " "))[1] "new object: 12059 cells"cmp.object <- NormalizeData(cmp.object, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|Find variable features
cmp.object <- FindVariableFeatures(cmp.object, selection.method = "vst", nfeatures = 2000)Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|top10 <- head(VariableFeatures(cmp.object), 10)
plot1 <- VariableFeaturePlot(cmp.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2
Scale data (linear transformation)
We don’t have to worry about comparing library depths, so we’ll just do normalization/Scale data
Regressing out nFeature_RNA, nCount_RNA
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Centering and scaling data matrix
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|================================================================================================================================================================================================================| 100%Save raw object
saveRDS(cmp.object, file = paste0(projectName, "_raw.RDS"))cmp.object <- RunPCA(cmp.object, features = VariableFeatures(cmp.object), ndims.print = 1:5, nfeatures.print = 5)PC_ 1
Positive: Vamp5, Nkg7, Car2, Apoe, Ctla2a
Negative: Lgals3, Aif1, Id2, Cst3, H2-Aa
PC_ 2
Positive: Prtn3, Ctsg, H2afy, Mpo, Emb
Negative: Ube2c, Cenpf, Nusap1, Mki67, Cenpa
PC_ 3
Positive: Pf4, Tmsb4x, Cavin2, Serpine2, Rap1b
Negative: Plac8, Cks2, Cenpa, Ube2c, Tubb4b
PC_ 4
Positive: Csrp3, Car1, Jun, Apoe, Jund
Negative: H2afz, Hmgn2, Birc5, Hmgb1, Tuba1b
PC_ 5
Positive: Pclaf, Tyms, Rrm2, Tk1, Pcna
Negative: Hist1h2bc, Tsc22d1, Smim14, Ccnb2, Serpinb1a DimPlot(cmp.object, reduction = "pca", group.by = "orig.ident")
VizDimLoadings(cmp.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 3)
Calculate dimensionality
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)Number of PCs describing X% of variance
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)
Add cluster IDs from Seurat v1
Exported cell IDs for clusters 3, 17, 10, 11 from Seurat v1. Will add these IDs as a metadata column.
Create column “clust.ID” and populate with 0’s. Then import IDs for clusters
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance:", length(which(cumsum(tot.var) <= 80)))[1] "Num pcs for 80% variance:18"paste0("Num pcs for 85% variance:", length(which(cumsum(tot.var) <= 85)))[1] "Num pcs for 85% variance:25"paste0("Num pcs for 90% variance:", length(which(cumsum(tot.var) <= 90)))[1] "Num pcs for 90% variance:33"paste0("Num pcs for 95% variance:", length(which(cumsum(tot.var) <= 95)))[1] "Num pcs for 95% variance:41"Add new metadata column
clust3.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster3cellIDs.txt", col.names = "clust03")
clust3.cells <- sapply(clust3.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust17.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster17cellIDs.txt", col.names = "clust17")
clust17.cells <- sapply(clust17.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust10.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster10cellIDs.txt", col.names = "clust10")
clust10.cells <- sapply(clust10.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust11.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster11cellIDs.txt", col.names = "clust11")
clust11.cells <- sapply(clust11.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))now map new ids
cmp.object@meta.data['clust.ID'] <- 0
head(cmp.object@meta.data)Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geomdo numbers make sense?
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust3.cells] <- 3
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust17.cells] <- 17
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust10.cells] <- 10
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust11.cells] <- 11Total var 90%
Neighborhood and umap
set total.var <- 90%
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 10,])[1] 1049nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 11,])[1] 1118nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 17,])[1] 883nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 3,])[1] 1931Plot UMAP
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)Computing nearest neighbor graphComputing SNNcmp.object <- FindClusters(cmp.object, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8502
Number of communities: 9
Elapsed time: 1 secondscmp.object <- RunUMAP(cmp.object, dims = 1: ndims)14:08:12 UMAP embedding parameters a = 0.9922 b = 1.112
14:08:12 Read 12059 rows and found 33 numeric columns
14:08:12 Using Annoy for neighbor search, n_neighbors = 30
14:08:12 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:08:13 Writing NN index file to temp file /var/folders/4f/fwrj6fnn1dn4g8wsf0zv563hjsvl24/T//Rtmp5gGDJN/file582a2b2d9d6c
14:08:14 Searching Annoy index using 1 thread, search_k = 3000
14:08:16 Annoy recall = 100%
14:08:17 Commencing smooth kNN distance calibration using 1 thread
14:08:17 Initializing from normalized Laplacian + noise
14:08:18 Commencing optimization for 200 epochs, with 513294 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:08:23 Optimization finishedfor(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8502
Number of communities: 9
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7974
Number of communities: 17
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7652
Number of communities: 22
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7404
Number of communities: 27
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7189
Number of communities: 30
Elapsed time: 1 secondssaveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))Clustree
what’s the max resolution we can achieve while keepign clusters stable?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
plot(myplot)
}
}
I think I’m liking res.1.0 from this. Although how much does this change if I use fewer PCs…
for each resolution, number/percentage of cells in each cluster?
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") +
scale_color_continuous(low = 'red3', high = 'white') +
ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()null device
1 For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
colnames(stats.df) <- c("num_cells", "pct_pop")
rownames(stats.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(stats.df)){
num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
pct.x <- as.integer(num.x / tot.cells *100)
# print(pct.x)
stats.df[row.id, "num_cells"] <- num.x
stats.df[row.id, "pct_pop"] <- pct.x
}
print(stats.df)
}
}Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
print(enrich.df)
}
}NADimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)
Total var 85%
Neighborhood and umap
set total.var <- 85%
DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 85))Plot UMAP
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 85))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)Computing nearest neighbor graphComputing SNNcmp.object <- FindClusters(cmp.object, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8508
Number of communities: 9
Elapsed time: 2 secondscmp.object <- RunUMAP(cmp.object, dims = 1: ndims)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
10:37:55 UMAP embedding parameters a = 0.9922 b = 1.112
10:37:55 Read 12059 rows and found 25 numeric columns
10:37:55 Using Annoy for neighbor search, n_neighbors = 30
10:37:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:37:57 Writing NN index file to temp file /var/folders/4f/fwrj6fnn1dn4g8wsf0zv563hjsvl24/T//RtmpjY0Xgs/file912c3cf1e81b
10:37:57 Searching Annoy index using 1 thread, search_k = 3000
10:38:01 Annoy recall = 100%
10:38:01 Commencing smooth kNN distance calibration using 1 thread
10:38:02 Initializing from normalized Laplacian + noise
10:38:02 Commencing optimization for 200 epochs, with 500658 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8508
Number of communities: 9
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8026
Number of communities: 18
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7700
Number of communities: 22
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7461
Number of communities: 28
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7257
Number of communities: 33
Elapsed time: 1 secondsClustree
what’s the max resolution we can achieve while keepign clusters stable?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
plot.title <- paste0(projectName, "dim", ndims, "res", gsub("RNA_snn_res", "", meta.col))
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(plot.title)
plot(myplot)
png(filename = paste0(plot.title, ".png"), height = 800, width = 800)
plot(DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette,
pt.size = 1.5
) +
ggtitle(plot.title)
)
dev.off()
}
}
for each resolution, number/percentage of cells in each cluster?
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") +
scale_color_continuous(low = 'red3', high = 'white') +
ggtitle(plot.title)
plot(my.clustree)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)plot(my.clustree)
dev.off()quartz_off_screen
2 
Store session info
sink(paste0(projectName, "_sessionInfo.txt"))
sessionInfo()
sink()Identify variable genes for new biomark
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
colnames(stats.df) <- c("num_cells", "pct_pop")
rownames(stats.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(stats.df)){
num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
pct.x <- as.integer(num.x / tot.cells *100)
# print(pct.x)
stats.df[row.id, "num_cells"] <- num.x
stats.df[row.id, "pct_pop"] <- pct.x
}
print(stats.df)
}
}source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/read_10XGenomics_data.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/PercentVariance.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/Mouse2Human_idconversion.R")Gene profiles of clusters
set ident at res = 1 and get markers
Idents(cmp.object) <- "RNA_snn_res.1"```r
setwd(\~/Desktop/10XGenomicsData/cellRanger/\) # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = \CMPm2\)
object.data <-Read10X(data_file.list)
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```r
```r
cmp.object<- CreateSeuratObject(counts = object.data, min.cells = 3, min.genes = 200, project = projectName)
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Clean up to free memory
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```r
```r
remove(object.data)
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Add mitochondrial metadata and plot some basic features
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```r
```r
cmp.object[[\percent.mt\]] <- PercentageFeatureSet(cmp.object, pattern = \^mt-\)
VlnPlot(cmp.object, features = c(\nFeature_RNA\, \nCount_RNA\, \percent.mt\), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
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" />
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucGxvdDEgPC0gRmVhdHVyZVNjYXR0ZXIoY21wLm9iamVjdCwgZmVhdHVyZTEgPSBcXG5Db3VudF9STkFcXCwgZmVhdHVyZTIgPSBcXHBlcmNlbnQubXRcXCwgZ3JvdXAuYnkgPSBcXG9yaWcuaWRlbnRcXCwgcHQuc2l6ZSA9IDAuMDEpXG5wbG90MiA8LSBGZWF0dXJlU2NhdHRlcihjbXAub2JqZWN0LCBmZWF0dXJlMSA9IFxcbkNvdW50X1JOQVxcLCBmZWF0dXJlMiA9IFxcbkZlYXR1cmVfUk5BXFwsIGdyb3VwLmJ5ID0gXFxvcmlnLmlkZW50XFwsIHB0LnNpemUgPSAwLjAxKVxucGxvdDEgKyBwbG90MlxuYGBgXG5gYGAifQ== -->
```r
```r
plot1 <- FeatureScatter(cmp.object, feature1 = \nCount_RNA\, feature2 = \percent.mt\, group.by = \orig.ident\, pt.size = 0.01)
plot2 <- FeatureScatter(cmp.object, feature1 = \nCount_RNA\, feature2 = \nFeature_RNA\, group.by = \orig.ident\, pt.size = 0.01)
plot1 + plot2
<!-- rnb-source-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
We don't have to worry about comparing library depths, so we'll just do normalization/Scale data
remove low quality cells
require: nFeature_RNA between 200 and 4000 (inclusive)
require: percent.mt <=5
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucHJpbnQocGFzdGUoXFxvcmlnaW5hbCBvYmplY3Q6XFwsIG5yb3coY21wLm9iamVjdEBtZXRhLmRhdGEpLCBcXGNlbGxzXFwsIHNlcCA9IFxcIFxcKSlcbmBgYFxuYGBgIn0= -->
```r
```r
print(paste(\original object:\, nrow(cmp.object@meta.data), \cells\, sep = \ \))
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIFxcb3JpZ2luYWwgb2JqZWN0OiAxMjU0MCBjZWxsc1xcXG4ifQ== -->
[1] object: 12540 cells
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY21wLm9iamVjdCA8LSBzdWJzZXQoY21wLm9iamVjdCwgXG5cdFx0XHRcdFx0XHRcdFx0XHRcdFx0XHRzdWJzZXQgPSBuRmVhdHVyZV9STkEgPj0yMDAgJiBcblx0XHRcdFx0XHRcdFx0XHRcdFx0XHRcdFx0bkZlYXR1cmVfUk5BIDw9IDQwMDAgJiBcblx0XHRcdFx0XHRcdFx0XHRcdFx0XHRcdFx0cGVyY2VudC5tdCA8PSA1XG5cdFx0XHRcdFx0XHRcdFx0XHRcdFx0XHQpXG5wcmludChwYXN0ZShcXG5ldyBvYmplY3Q6XFwsIG5yb3coY21wLm9iamVjdEBtZXRhLmRhdGEpLCBcXGNlbGxzXFwsIHNlcCA9IFxcIFxcKSlcbmBgYFxuYGBgIn0= -->
```r
```r
cmp.object <- subset(cmp.object,
subset = nFeature_RNA >=200 &
nFeature_RNA <= 4000 &
percent.mt <= 5
)
print(paste(\new object:\, nrow(cmp.object@meta.data), \cells\, sep = \ \))
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIFxcbmV3IG9iamVjdDogMTIwNTkgY2VsbHNcXFxuIn0= -->
[1] object: 12059 cells
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY21wLm9iamVjdCA8LSBOb3JtYWxpemVEYXRhKGNtcC5vYmplY3QsIG5vcm1hbGl6YXRpb24ubWV0aG9kID0gXFxMb2dOb3JtYWxpemVcXCwgc2NhbGUuZmFjdG9yID0gMTAwMDApXG5gYGBcbmBgYCJ9 -->
```r
```r
cmp.object <- NormalizeData(cmp.object, normalization.method = \LogNormalize\, scale.factor = 10000)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Find variable features
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY21wLm9iamVjdCA8LSBGaW5kVmFyaWFibGVGZWF0dXJlcyhjbXAub2JqZWN0LCBzZWxlY3Rpb24ubWV0aG9kID0gXFx2c3RcXCwgbmZlYXR1cmVzID0gMjAwMClcbnRvcDEwIDwtIGhlYWQoVmFyaWFibGVGZWF0dXJlcyhjbXAub2JqZWN0KSwgMTApXG5wbG90MSA8LSBWYXJpYWJsZUZlYXR1cmVQbG90KGNtcC5vYmplY3QpXG5wbG90MiA8LSBMYWJlbFBvaW50cyhwbG90ID0gcGxvdDEsIHBvaW50cyA9IHRvcDEwLCByZXBlbCA9IFRSVUUpXG5wbG90MSArIHBsb3QyXG5cbmBgYFxuYGBgIn0= -->
```r
```r
cmp.object <- FindVariableFeatures(cmp.object, selection.method = \vst\, nfeatures = 2000)
top10 <- head(VariableFeatures(cmp.object), 10)
plot1 <- VariableFeaturePlot(cmp.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
=======
Idents(cmp.object) <- "RNA_snn_res.1"
cmp.allmarkers.res1 <- FindAllMarkers(cmp.object)
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~23s
|++ | 2 % ~23s
|++ | 3 % ~22s
|+++ | 4 % ~21s
|+++ | 5 % ~21s
|++++ | 6 % ~20s
|++++ | 7 % ~20s
|+++++ | 8 % ~20s
|+++++ | 9 % ~19s
|++++++ | 10% ~20s
|++++++ | 11% ~19s
|+++++++ | 12% ~19s
|+++++++ | 14% ~19s
|++++++++ | 15% ~19s
|++++++++ | 16% ~18s
|+++++++++ | 17% ~18s
|+++++++++ | 18% ~18s
|++++++++++ | 19% ~18s
|++++++++++ | 20% ~17s
|+++++++++++ | 21% ~17s
|+++++++++++ | 22% ~17s
|++++++++++++ | 23% ~17s
|++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~16s
|++++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~16s
|+++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~15s
|++++++++++++++++ | 30% ~15s
|++++++++++++++++ | 31% ~15s
|+++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~14s
|++++++++++++++++++ | 34% ~14s
|++++++++++++++++++ | 35% ~14s
|+++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~13s
|++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|+++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~13s
|++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|+++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|+++++++++++++++++++++++++ | 50% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|+++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|++++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|+++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|++++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|+++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|++++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|+++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21s
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~28s
|++ | 2 % ~28s
|++ | 3 % ~28s
|+++ | 4 % ~27s
|+++ | 5 % ~27s
|++++ | 6 % ~26s
|++++ | 7 % ~27s
|+++++ | 9 % ~27s
|+++++ | 10% ~26s
|++++++ | 11% ~26s
|++++++ | 12% ~25s
|+++++++ | 13% ~25s
|+++++++ | 14% ~25s
|++++++++ | 15% ~24s
|++++++++ | 16% ~24s
|+++++++++ | 17% ~24s
|++++++++++ | 18% ~23s
|++++++++++ | 19% ~23s
|+++++++++++ | 20% ~23s
|+++++++++++ | 21% ~22s
|++++++++++++ | 22% ~22s
|++++++++++++ | 23% ~22s
|+++++++++++++ | 24% ~21s
|+++++++++++++ | 26% ~21s
|++++++++++++++ | 27% ~21s
|++++++++++++++ | 28% ~21s
|+++++++++++++++ | 29% ~21s
|+++++++++++++++ | 30% ~20s
|++++++++++++++++ | 31% ~20s
|++++++++++++++++ | 32% ~20s
|+++++++++++++++++ | 33% ~19s
|++++++++++++++++++ | 34% ~19s
|++++++++++++++++++ | 35% ~19s
|+++++++++++++++++++ | 36% ~18s
|+++++++++++++++++++ | 37% ~18s
|++++++++++++++++++++ | 38% ~18s
|++++++++++++++++++++ | 39% ~17s
|+++++++++++++++++++++ | 40% ~17s
|+++++++++++++++++++++ | 41% ~17s
|++++++++++++++++++++++ | 43% ~16s
|++++++++++++++++++++++ | 44% ~16s
|+++++++++++++++++++++++ | 45% ~16s
|+++++++++++++++++++++++ | 46% ~16s
|++++++++++++++++++++++++ | 47% ~15s
|++++++++++++++++++++++++ | 48% ~15s
|+++++++++++++++++++++++++ | 49% ~15s
|+++++++++++++++++++++++++ | 50% ~14s
|++++++++++++++++++++++++++ | 51% ~14s
|+++++++++++++++++++++++++++ | 52% ~14s
|+++++++++++++++++++++++++++ | 53% ~13s
|++++++++++++++++++++++++++++ | 54% ~13s
|++++++++++++++++++++++++++++ | 55% ~13s
|+++++++++++++++++++++++++++++ | 56% ~13s
|+++++++++++++++++++++++++++++ | 57% ~12s
|++++++++++++++++++++++++++++++ | 59% ~12s
|++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 61% ~11s
|+++++++++++++++++++++++++++++++ | 62% ~11s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~10s
|+++++++++++++++++++++++++++++++++ | 65% ~10s
|+++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 68% ~09s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 70% ~09s
|++++++++++++++++++++++++++++++++++++ | 71% ~08s
|+++++++++++++++++++++++++++++++++++++ | 72% ~08s
|+++++++++++++++++++++++++++++++++++++ | 73% ~08s
|++++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 76% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=29s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~08s
|+++ | 5 % ~08s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 11% ~07s
|++++++ | 12% ~07s
|+++++++ | 13% ~07s
|+++++++ | 14% ~07s
|++++++++ | 15% ~07s
|++++++++ | 16% ~07s
|+++++++++ | 17% ~06s
|+++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 31% ~06s
|++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~05s
|++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~09s
|++ | 2 % ~09s
|++ | 3 % ~11s
|+++ | 5 % ~11s
|+++ | 6 % ~10s
|++++ | 7 % ~10s
|+++++ | 8 % ~10s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~09s
|+++++++ | 13% ~09s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|++++++++++++++ | 26% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|+++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 43% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 4 % ~11s
|+++ | 6 % ~11s
|++++ | 7 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|+++++ | 10% ~10s
|++++++ | 11% ~10s
|+++++++ | 12% ~10s
|+++++++ | 13% ~10s
|++++++++ | 14% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~10s
|++++++++++ | 20% ~10s
|+++++++++++ | 21% ~10s
|++++++++++++ | 22% ~10s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 24% ~09s
|+++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~07s
|++++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|++++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|+++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s
Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~20s
|++ | 2 % ~19s
|++ | 3 % ~19s
|+++ | 5 % ~18s
|+++ | 6 % ~18s
|++++ | 7 % ~18s
|++++ | 8 % ~18s
|+++++ | 9 % ~18s
|++++++ | 10% ~17s
|++++++ | 11% ~18s
|+++++++ | 12% ~18s
|+++++++ | 14% ~17s
|++++++++ | 15% ~17s
|++++++++ | 16% ~17s
|+++++++++ | 17% ~17s
|++++++++++ | 18% ~16s
|++++++++++ | 19% ~16s
|+++++++++++ | 20% ~16s
|+++++++++++ | 22% ~15s
|++++++++++++ | 23% ~15s
|++++++++++++ | 24% ~15s
|+++++++++++++ | 25% ~15s
|++++++++++++++ | 26% ~15s
|++++++++++++++ | 27% ~14s
|+++++++++++++++ | 28% ~14s
|+++++++++++++++ | 30% ~14s
|++++++++++++++++ | 31% ~14s
|++++++++++++++++ | 32% ~13s
|+++++++++++++++++ | 33% ~13s
|++++++++++++++++++ | 34% ~13s
|++++++++++++++++++ | 35% ~13s
|+++++++++++++++++++ | 36% ~12s
|+++++++++++++++++++ | 38% ~12s
|++++++++++++++++++++ | 39% ~12s
|++++++++++++++++++++ | 40% ~12s
|+++++++++++++++++++++ | 41% ~12s
|++++++++++++++++++++++ | 42% ~11s
|++++++++++++++++++++++ | 43% ~11s
|+++++++++++++++++++++++ | 44% ~11s
|+++++++++++++++++++++++ | 45% ~11s
|++++++++++++++++++++++++ | 47% ~11s
|++++++++++++++++++++++++ | 48% ~10s
|+++++++++++++++++++++++++ | 49% ~10s
|+++++++++++++++++++++++++ | 50% ~10s
|++++++++++++++++++++++++++ | 51% ~10s
|+++++++++++++++++++++++++++ | 52% ~09s
|+++++++++++++++++++++++++++ | 53% ~09s
|++++++++++++++++++++++++++++ | 55% ~09s
|++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~08s
|+++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|++++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|++++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s
Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~07s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~10s
|++++ | 6 % ~09s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|+++++ | 10% ~08s
|++++++ | 11% ~08s
|++++++ | 12% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 16% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 18% ~07s
|++++++++++ | 19% ~07s
|+++++++++++ | 20% ~07s
|+++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~22s
|++ | 2 % ~22s
|++ | 3 % ~21s
|+++ | 5 % ~21s
|+++ | 6 % ~20s
|++++ | 7 % ~20s
|+++++ | 8 % ~20s
|+++++ | 9 % ~20s
|++++++ | 10% ~20s
|++++++ | 12% ~20s
|+++++++ | 13% ~20s
|+++++++ | 14% ~19s
|++++++++ | 15% ~19s
|+++++++++ | 16% ~19s
|+++++++++ | 17% ~19s
|++++++++++ | 19% ~19s
|++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|++++++++++++ | 22% ~18s
|++++++++++++ | 23% ~17s
|+++++++++++++ | 24% ~17s
|+++++++++++++ | 26% ~17s
|++++++++++++++ | 27% ~16s
|++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~16s
|++++++++++++++++ | 30% ~15s
|++++++++++++++++ | 31% ~15s
|+++++++++++++++++ | 33% ~15s
|+++++++++++++++++ | 34% ~15s
|++++++++++++++++++ | 35% ~14s
|+++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|++++++++++++++++++++ | 38% ~13s
|++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|+++++++++++++++++++++ | 42% ~12s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|++++++++++++++++++++++++ | 47% ~11s
|++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|+++++++++++++++++++++++++ | 50% ~11s
|++++++++++++++++++++++++++ | 51% ~10s
|+++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|++++++++++++++++++++++++++++ | 55% ~09s
|++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|++++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~08s
|+++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|++++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s
Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 2 % ~11s
|++ | 3 % ~10s
|+++ | 5 % ~10s
|+++ | 6 % ~10s
|++++ | 7 % ~10s
|++++ | 8 % ~10s
|+++++ | 9 % ~10s
|++++++ | 10% ~10s
|++++++ | 11% ~10s
|+++++++ | 12% ~09s
|+++++++ | 14% ~09s
|++++++++ | 15% ~09s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|++++++++++ | 18% ~09s
|++++++++++ | 19% ~09s
|+++++++++++ | 20% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~08s
|++++++++++++ | 24% ~08s
|+++++++++++++ | 25% ~08s
|++++++++++++++ | 26% ~08s
|++++++++++++++ | 27% ~08s
|+++++++++++++++ | 28% ~08s
|+++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|+++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|++++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|+++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|+++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s
Calculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~01m 15s
|+ | 2 % ~01m 14s
|++ | 3 % ~01m 16s
|++ | 4 % ~01m 14s
|+++ | 5 % ~01m 13s
|+++ | 6 % ~01m 11s
|++++ | 7 % ~01m 10s
|++++ | 8 % ~01m 09s
|+++++ | 9 % ~01m 09s
|+++++ | 10% ~01m 08s
|++++++ | 11% ~01m 07s
|++++++ | 12% ~01m 06s
|+++++++ | 13% ~01m 06s
|+++++++ | 14% ~01m 05s
|++++++++ | 15% ~01m 04s
|++++++++ | 16% ~01m 03s
|+++++++++ | 17% ~01m 03s
|+++++++++ | 18% ~01m 03s
|++++++++++ | 19% ~01m 02s
|++++++++++ | 20% ~01m 01s
|+++++++++++ | 21% ~01m 00s
|+++++++++++ | 22% ~60s
|++++++++++++ | 23% ~59s
|++++++++++++ | 24% ~59s
|+++++++++++++ | 25% ~58s
|+++++++++++++ | 26% ~57s
|++++++++++++++ | 27% ~57s
|++++++++++++++ | 28% ~56s
|+++++++++++++++ | 29% ~56s
|+++++++++++++++ | 30% ~55s
|++++++++++++++++ | 31% ~54s
|++++++++++++++++ | 32% ~54s
|+++++++++++++++++ | 33% ~53s
|+++++++++++++++++ | 34% ~52s
|++++++++++++++++++ | 35% ~52s
|++++++++++++++++++ | 36% ~51s
|+++++++++++++++++++ | 37% ~50s
|+++++++++++++++++++ | 38% ~49s
|++++++++++++++++++++ | 39% ~48s
|++++++++++++++++++++ | 40% ~48s
|+++++++++++++++++++++ | 41% ~47s
|+++++++++++++++++++++ | 42% ~46s
|++++++++++++++++++++++ | 43% ~45s
|++++++++++++++++++++++ | 44% ~44s
|+++++++++++++++++++++++ | 45% ~44s
|+++++++++++++++++++++++ | 46% ~43s
|++++++++++++++++++++++++ | 47% ~42s
|++++++++++++++++++++++++ | 48% ~41s
|+++++++++++++++++++++++++ | 49% ~40s
|+++++++++++++++++++++++++ | 50% ~40s
|++++++++++++++++++++++++++ | 51% ~39s
|++++++++++++++++++++++++++ | 52% ~38s
|+++++++++++++++++++++++++++ | 53% ~37s
|+++++++++++++++++++++++++++ | 54% ~36s
|++++++++++++++++++++++++++++ | 55% ~35s
|++++++++++++++++++++++++++++ | 56% ~35s
|+++++++++++++++++++++++++++++ | 57% ~34s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~31s
|+++++++++++++++++++++++++++++++ | 62% ~30s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~28s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~27s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~24s
|+++++++++++++++++++++++++++++++++++ | 70% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 16s
Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~17s
|++ | 2 % ~17s
|++ | 3 % ~16s
|+++ | 4 % ~16s
|+++ | 6 % ~16s
|++++ | 7 % ~16s
|++++ | 8 % ~16s
|+++++ | 9 % ~16s
|+++++ | 10% ~15s
|++++++ | 11% ~15s
|+++++++ | 12% ~15s
|+++++++ | 13% ~15s
|++++++++ | 14% ~16s
|++++++++ | 16% ~16s
|+++++++++ | 17% ~15s
|+++++++++ | 18% ~15s
|++++++++++ | 19% ~15s
|++++++++++ | 20% ~14s
|+++++++++++ | 21% ~14s
|++++++++++++ | 22% ~14s
|++++++++++++ | 23% ~14s
|+++++++++++++ | 24% ~14s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~13s
|+++++++++++++++ | 29% ~13s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|+++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|++++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~10s
|++++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|+++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s
Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~25s
|++ | 2 % ~25s
|++ | 3 % ~25s
|+++ | 4 % ~24s
|+++ | 5 % ~24s
|++++ | 6 % ~24s
|++++ | 7 % ~23s
|+++++ | 8 % ~23s
|+++++ | 9 % ~23s
|++++++ | 10% ~23s
|++++++ | 11% ~23s
|+++++++ | 12% ~23s
|+++++++ | 13% ~22s
|++++++++ | 14% ~22s
|++++++++ | 15% ~22s
|+++++++++ | 16% ~21s
|+++++++++ | 17% ~21s
Find top.var genes in cmp.top100markers.res1
cmp.top100markers.res1 <- cmp.allmarkers.res1 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
cmp.top100markers.res1 <- cmp.top100markers.res1[cmp.top100markers.res1$p_val_adj <= 0.05, ]
for(cluster in sort(as.numeric(unique(cmp.top100markers.res1$cluster)))){
num.hits <- nrow(cmp.top100markers.res1[cmp.top100markers.res1$cluster == cluster, ])
print(paste("cluster", cluster, "has", num.hits, "genes"))
cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
try(
xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")],
file = paste0(projectName, "_dim", ndims, "_FindMarkersTop100.xlsx"),
sheetName = paste0("clst", cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
)
}
[1] "cluster 1 has 99 genes"
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~07s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~06s
|++++++ | 11% ~06s
|+++++++ | 12% ~06s
|+++++++ | 13% ~06s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 25% ~05s
|++++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s
[1] "cluster 2 has 99 genes"
| | 0 % ~calculating
|+ | 1 % ~28s
|++ | 2 % ~28s
|++ | 3 % ~27s
|+++ | 4 % ~27s
|+++ | 5 % ~27s
|++++ | 6 % ~26s
|++++ | 7 % ~26s
|+++++ | 9 % ~26s
|+++++ | 10% ~25s
|++++++ | 11% ~25s
|++++++ | 12% ~25s
|+++++++ | 13% ~24s
|+++++++ | 14% ~24s
|++++++++ | 15% ~24s
|++++++++ | 16% ~24s
|+++++++++ | 17% ~23s
|++++++++++ | 18% ~23s
|++++++++++ | 19% ~23s
|+++++++++++ | 20% ~22s
|+++++++++++ | 21% ~22s
|++++++++++++ | 22% ~22s
|++++++++++++ | 23% ~21s
|+++++++++++++ | 24% ~21s
|+++++++++++++ | 26% ~21s
|++++++++++++++ | 27% ~21s
|++++++++++++++ | 28% ~20s
|+++++++++++++++ | 29% ~20s
|+++++++++++++++ | 30% ~20s
|++++++++++++++++ | 31% ~19s
|++++++++++++++++ | 32% ~19s
|+++++++++++++++++ | 33% ~19s
|++++++++++++++++++ | 34% ~19s
|++++++++++++++++++ | 35% ~18s
|+++++++++++++++++++ | 36% ~18s
|+++++++++++++++++++ | 37% ~18s
|++++++++++++++++++++ | 38% ~17s
|++++++++++++++++++++ | 39% ~17s
|+++++++++++++++++++++ | 40% ~17s
|+++++++++++++++++++++ | 41% ~16s
|++++++++++++++++++++++ | 43% ~16s
|++++++++++++++++++++++ | 44% ~16s
|+++++++++++++++++++++++ | 45% ~15s
|+++++++++++++++++++++++ | 46% ~15s
|++++++++++++++++++++++++ | 47% ~15s
|++++++++++++++++++++++++ | 48% ~14s
|+++++++++++++++++++++++++ | 49% ~14s
|+++++++++++++++++++++++++ | 50% ~14s
|++++++++++++++++++++++++++ | 51% ~14s
|+++++++++++++++++++++++++++ | 52% ~13s
|+++++++++++++++++++++++++++ | 53% ~13s
|++++++++++++++++++++++++++++ | 54% ~13s
|++++++++++++++++++++++++++++ | 55% ~12s
|+++++++++++++++++++++++++++++ | 56% ~12s
|+++++++++++++++++++++++++++++ | 57% ~12s
|++++++++++++++++++++++++++++++ | 59% ~11s
|++++++++++++++++++++++++++++++ | 60% ~11s
|+++++++++++++++++++++++++++++++ | 61% ~11s
|+++++++++++++++++++++++++++++++ | 62% ~11s
|++++++++++++++++++++++++++++++++ | 63% ~10s
|++++++++++++++++++++++++++++++++ | 64% ~10s
|+++++++++++++++++++++++++++++++++ | 65% ~10s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 68% ~09s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|++++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~08s
|+++++++++++++++++++++++++++++++++++++ | 72% ~08s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|++++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 76% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=27s
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.poi.util.SAXHelper (file:/Users/heustonef/Library/R/4.0/library/xlsxjars/java/poi-ooxml-3.10.1-20140818.jar) to constructor com.sun.org.apache.xerces.internal.util.SecurityManager()
WARNING: Please consider reporting this to the maintainers of org.apache.poi.util.SAXHelper
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
[1] "cluster 3 has 99 genes"
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 11% ~07s
|++++++ | 12% ~07s
|+++++++ | 13% ~07s
|+++++++ | 14% ~06s
|++++++++ | 15% ~06s
|++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|+++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
[1] "cluster 4 has 97 genes"
| | 0 % ~calculating
|+ | 1 % ~09s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 5 % ~09s
|+++ | 6 % ~08s
|++++ | 7 % ~08s
|+++++ | 8 % ~08s
|+++++ | 9 % ~08s
|++++++ | 10% ~08s
|++++++ | 11% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~07s
|++++++++++ | 18% ~07s
|++++++++++ | 20% ~07s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|++++++++++++++ | 26% ~07s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|+++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 43% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s
[1] "cluster 5 has 98 genes"
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 4 % ~11s
|+++ | 6 % ~11s
|++++ | 7 % ~11s
|++++ | 8 % ~10s
|+++++ | 9 % ~10s
|+++++ | 10% ~10s
|++++++ | 11% ~10s
|+++++++ | 12% ~10s
|+++++++ | 13% ~10s
|++++++++ | 14% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~09s
|++++++++++ | 19% ~09s
|++++++++++ | 20% ~09s
|+++++++++++ | 21% ~09s
|++++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 24% ~09s
|+++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|++++++++++++++ | 28% ~08s
|+++++++++++++++ | 29% ~08s
|+++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 37% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|++++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s
[1] "cluster 6 has 98 genes"
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~14s
|++ | 3 % ~13s
|+++ | 5 % ~13s
|+++ | 6 % ~13s
|++++ | 7 % ~13s
|++++ | 8 % ~12s
|+++++ | 9 % ~12s
|++++++ | 10% ~12s
|++++++ | 11% ~12s
|+++++++ | 12% ~12s
|+++++++ | 14% ~12s
|++++++++ | 15% ~11s
|++++++++ | 16% ~11s
|+++++++++ | 17% ~11s
|++++++++++ | 18% ~11s
|++++++++++ | 19% ~11s
|+++++++++++ | 20% ~11s
|+++++++++++ | 22% ~10s
|++++++++++++ | 23% ~10s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|++++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|+++++++++++++++ | 28% ~10s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s
[1] "cluster 7 has 96 genes"
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 8 % ~05s
|+++++ | 9 % ~05s
|+++++ | 10% ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~05s
|+++++++ | 13% ~04s
|+++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|+++++++++++ | 20% ~04s
|+++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
[1] "cluster 8 has 99 genes"
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~15s
|++ | 3 % ~14s
|+++ | 5 % ~14s
|+++ | 6 % ~13s
|++++ | 7 % ~13s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 10% ~12s
|++++++ | 12% ~12s
|+++++++ | 13% ~12s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~12s
|+++++++++ | 17% ~11s
|++++++++++ | 19% ~11s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|++++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 24% ~11s
|+++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|+++++++++++++++++ | 33% ~09s
|+++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s
[1] "cluster 9 has 97 genes"
| | 0 % ~calculating
|+ | 1 % ~09s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 5 % ~09s
|+++ | 6 % ~09s
|++++ | 7 % ~09s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~08s
|+++++++ | 12% ~08s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|++++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|+++++++++++++++ | 28% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~06s
|++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s
[1] "cluster 10 has 100 genes"
| | 0 % ~calculating
|+ | 1 % ~01m 14s
|+ | 2 % ~01m 13s
|++ | 3 % ~01m 12s
|++ | 4 % ~01m 11s
|+++ | 5 % ~01m 10s
|+++ | 6 % ~01m 09s
|++++ | 7 % ~01m 08s
|++++ | 8 % ~01m 07s
|+++++ | 9 % ~01m 07s
|+++++ | 10% ~01m 06s
|++++++ | 11% ~01m 05s
|++++++ | 12% ~01m 05s
|+++++++ | 13% ~01m 04s
|+++++++ | 14% ~01m 03s
|++++++++ | 15% ~01m 03s
|++++++++ | 16% ~01m 03s
|+++++++++ | 17% ~01m 02s
|+++++++++ | 18% ~01m 01s
|++++++++++ | 19% ~60s
|++++++++++ | 20% ~59s
|+++++++++++ | 21% ~58s
|+++++++++++ | 22% ~58s
|++++++++++++ | 23% ~57s
|++++++++++++ | 24% ~56s
|+++++++++++++ | 25% ~55s
|+++++++++++++ | 26% ~54s
|++++++++++++++ | 27% ~54s
|++++++++++++++ | 28% ~53s
|+++++++++++++++ | 29% ~52s
|+++++++++++++++ | 30% ~52s
|++++++++++++++++ | 31% ~51s
|++++++++++++++++ | 32% ~50s
|+++++++++++++++++ | 33% ~50s
|+++++++++++++++++ | 34% ~49s
|++++++++++++++++++ | 35% ~48s
|++++++++++++++++++ | 36% ~47s
|+++++++++++++++++++ | 37% ~46s
|+++++++++++++++++++ | 38% ~46s
|++++++++++++++++++++ | 39% ~45s
|++++++++++++++++++++ | 40% ~44s
|+++++++++++++++++++++ | 41% ~43s
|+++++++++++++++++++++ | 42% ~43s
|++++++++++++++++++++++ | 43% ~42s
|++++++++++++++++++++++ | 44% ~41s
|+++++++++++++++++++++++ | 45% ~41s
|+++++++++++++++++++++++ | 46% ~40s
|++++++++++++++++++++++++ | 47% ~39s
|++++++++++++++++++++++++ | 48% ~38s
|+++++++++++++++++++++++++ | 49% ~38s
|+++++++++++++++++++++++++ | 50% ~37s
|++++++++++++++++++++++++++ | 51% ~36s
|++++++++++++++++++++++++++ | 52% ~35s
|+++++++++++++++++++++++++++ | 53% ~35s
|+++++++++++++++++++++++++++ | 54% ~34s
|++++++++++++++++++++++++++++ | 55% ~33s
|++++++++++++++++++++++++++++ | 56% ~32s
|+++++++++++++++++++++++++++++ | 57% ~32s
|+++++++++++++++++++++++++++++ | 58% ~31s
|++++++++++++++++++++++++++++++ | 59% ~30s
|++++++++++++++++++++++++++++++ | 60% ~30s
|+++++++++++++++++++++++++++++++ | 61% ~29s
|+++++++++++++++++++++++++++++++ | 62% ~28s
|++++++++++++++++++++++++++++++++ | 63% ~27s
|++++++++++++++++++++++++++++++++ | 64% ~27s
|+++++++++++++++++++++++++++++++++ | 65% ~26s
|+++++++++++++++++++++++++++++++++ | 66% ~25s
|++++++++++++++++++++++++++++++++++ | 67% ~24s
|++++++++++++++++++++++++++++++++++ | 68% ~24s
|+++++++++++++++++++++++++++++++++++ | 69% ~23s
|+++++++++++++++++++++++++++++++++++ | 70% ~22s
|++++++++++++++++++++++++++++++++++++ | 71% ~21s
|++++++++++++++++++++++++++++++++++++ | 72% ~21s
|+++++++++++++++++++++++++++++++++++++ | 73% ~20s
|+++++++++++++++++++++++++++++++++++++ | 74% ~19s
|++++++++++++++++++++++++++++++++++++++ | 75% ~18s
|++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 13s
[1] "cluster 11 has 98 genes"
| | 0 % ~calculating
|+ | 1 % ~18s
|++ | 2 % ~17s
|++ | 3 % ~17s
|+++ | 4 % ~17s
|+++ | 6 % ~16s
|++++ | 7 % ~16s
|++++ | 8 % ~16s
|+++++ | 9 % ~16s
|+++++ | 10% ~16s
|++++++ | 11% ~15s
|+++++++ | 12% ~15s
|+++++++ | 13% ~15s
|++++++++ | 14% ~15s
|++++++++ | 16% ~15s
|+++++++++ | 17% ~14s
|+++++++++ | 18% ~14s
|++++++++++ | 19% ~14s
|++++++++++ | 20% ~14s
|+++++++++++ | 21% ~14s
|++++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 24% ~13s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|+++++++++++++++ | 30% ~12s
|++++++++++++++++ | 31% ~12s
|+++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~10s
|++++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~07s
|+++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s
[1] "cluster 12 has 97 genes"
| | 0 % ~calculating
|+ | 1 % ~24s
|++ | 2 % ~23s
|++ | 3 % ~22s
|+++ | 4 % ~22s
|+++ | 5 % ~21s
|++++ | 6 % ~21s
|++++ | 7 % ~21s
|+++++ | 8 % ~20s
|+++++ | 9 % ~20s
|++++++ | 10% ~20s
|++++++ | 11% ~19s
|+++++++ | 12% ~20s
|+++++++ | 13% ~19s
|++++++++ | 14% ~19s
|++++++++ | 15% ~19s
|+++++++++ | 16% ~19s
|+++++++++ | 17% ~19s
|++++++++++ | 18% ~19s
|++++++++++ | 19% ~18s
|+++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|++++++++++++ | 22% ~18s
|++++++++++++ | 23% ~18s
|+++++++++++++ | 24% ~17s
|+++++++++++++ | 26% ~18s
|++++++++++++++ | 27% ~17s
|++++++++++++++ | 28% ~17s
|+++++++++++++++ | 29% ~17s
|+++++++++++++++ | 30% ~17s
|++++++++++++++++ | 31% ~16s
|++++++++++++++++ | 32% ~16s
|+++++++++++++++++ | 33% ~16s
|+++++++++++++++++ | 34% ~16s
|++++++++++++++++++ | 35% ~15s
|++++++++++++++++++ | 36% ~15s
|+++++++++++++++++++ | 37% ~15s
|+++++++++++++++++++ | 38% ~15s
|++++++++++++++++++++ | 39% ~14s
|++++++++++++++++++++ | 40% ~14s
|+++++++++++++++++++++ | 41% ~14s
|+++++++++++++++++++++ | 42% ~14s
|++++++++++++++++++++++ | 43% ~13s
|++++++++++++++++++++++ | 44% ~13s
|+++++++++++++++++++++++ | 45% ~13s
|+++++++++++++++++++++++ | 46% ~13s
|++++++++++++++++++++++++ | 47% ~13s
|++++++++++++++++++++++++ | 48% ~12s
|+++++++++++++++++++++++++ | 49% ~12s
|+++++++++++++++++++++++++ | 50% ~12s
|++++++++++++++++++++++++++ | 51% ~12s
|+++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|++++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~11s
|+++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|++++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~10s
|+++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|++++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~09s
|+++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|++++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~08s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|++++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|+++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|++++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=24s
[1] "cluster 13 has 99 genes"
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 4 % ~10s
|+++ | 5 % ~10s
|++++ | 6 % ~10s
|++++ | 7 % ~10s
|+++++ | 8 % ~10s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~09s
|+++++++ | 12% ~09s
|+++++++ | 13% ~09s
|++++++++ | 14% ~09s
|++++++++ | 15% ~09s
|+++++++++ | 16% ~09s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|+++++++++++++ | 24% ~08s
|+++++++++++++ | 26% ~08s
|++++++++++++++ | 27% ~08s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|+++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|+++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|+++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s
[1] "cluster 14 has 2 genes"
| | 0 % ~calculating
|++ | 2 % ~01s
|+++ | 4 % ~01s
|++++ | 7 % ~01s
|+++++ | 9 % ~01s
|++++++ | 11% ~01s
|+++++++ | 13% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 20% ~01s
|++++++++++++ | 22% ~01s
|+++++++++++++ | 24% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
[1] "cluster 15 has 98 genes"
| | 0 % ~calculating
|+ | 1 % ~14s
|+ | 2 % ~14s
|++ | 3 % ~13s
|++ | 4 % ~13s
|+++ | 5 % ~13s
|+++ | 6 % ~12s
|++++ | 7 % ~12s
|++++ | 8 % ~12s
|+++++ | 9 % ~12s
|+++++ | 10% ~12s
|++++++ | 11% ~12s
|++++++ | 12% ~12s
|+++++++ | 13% ~12s
|+++++++ | 14% ~11s
|++++++++ | 15% ~11s
|++++++++ | 16% ~11s
|+++++++++ | 17% ~11s
|+++++++++ | 18% ~11s
|++++++++++ | 19% ~11s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|+++++++++++++ | 26% ~10s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|+++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|+++++++++++++++++++++ | 42% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s
[1] "cluster 16 has 98 genes"
| | 0 % ~calculating
|+ | 1 % ~32s
|++ | 2 % ~31s
|++ | 3 % ~31s
|+++ | 4 % ~30s
|+++ | 5 % ~30s
|++++ | 6 % ~30s
|++++ | 7 % ~29s
|+++++ | 8 % ~29s
|+++++ | 9 % ~29s
|++++++ | 10% ~28s
|++++++ | 11% ~28s
|+++++++ | 12% ~29s
|+++++++ | 13% ~29s
|++++++++ | 14% ~28s
|++++++++ | 15% ~28s
|+++++++++ | 16% ~28s
|+++++++++ | 17% ~27s
|++++++++++ | 18% ~27s
|++++++++++ | 19% ~26s
|+++++++++++ | 20% ~26s
|+++++++++++ | 21% ~26s
|++++++++++++ | 22% ~25s
|++++++++++++ | 23% ~25s
|+++++++++++++ | 24% ~25s
|+++++++++++++ | 25% ~24s
|++++++++++++++ | 26% ~24s
|++++++++++++++ | 27% ~24s
|+++++++++++++++ | 28% ~23s
|+++++++++++++++ | 29% ~23s
|++++++++++++++++ | 30% ~23s
|++++++++++++++++ | 31% ~22s
|+++++++++++++++++ | 32% ~22s
|+++++++++++++++++ | 33% ~22s
|++++++++++++++++++ | 34% ~21s
|++++++++++++++++++ | 35% ~21s
|+++++++++++++++++++ | 36% ~21s
|+++++++++++++++++++ | 37% ~20s
|++++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 39% ~20s
|+++++++++++++++++++++ | 40% ~19s
|+++++++++++++++++++++ | 41% ~19s
|++++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 44% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|++++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 47% ~17s
|+++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~15s
|+++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~14s
|+++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~13s
|++++++++++++++++++++++++++++++ | 60% ~13s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|+++++++++++++++++++++++++++++++ | 62% ~13s
|++++++++++++++++++++++++++++++++ | 63% ~12s
|++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~12s
|+++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|++++++++++++++++++++++++++++++++++ | 68% ~11s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|+++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=33s
[1] "cluster 17 has 96 genes"
| | 0 % ~calculating
|+ | 1 % ~27s
|++ | 2 % ~27s
|++ | 3 % ~26s
|+++ | 4 % ~26s
|+++ | 5 % ~26s
|++++ | 6 % ~25s
|++++ | 8 % ~25s
|+++++ | 9 % ~27s
|+++++ | 10% ~26s
|++++++ | 11% ~26s
|++++++ | 12% ~26s
|+++++++ | 13% ~26s
|+++++++ | 14% ~26s
|++++++++ | 15% ~25s
|+++++++++ | 16% ~24s
|+++++++++ | 17% ~24s
|++++++++++ | 18% ~23s
|++++++++++ | 19% ~23s
|+++++++++++ | 20% ~23s
|+++++++++++ | 22% ~22s
|++++++++++++ | 23% ~22s
|++++++++++++ | 24% ~21s
|+++++++++++++ | 25% ~21s
|+++++++++++++ | 26% ~21s
|++++++++++++++ | 27% ~20s
|++++++++++++++ | 28% ~20s
|+++++++++++++++ | 29% ~20s
|++++++++++++++++ | 30% ~19s
|++++++++++++++++ | 31% ~19s
|+++++++++++++++++ | 32% ~19s
|+++++++++++++++++ | 33% ~18s
|++++++++++++++++++ | 34% ~18s
|++++++++++++++++++ | 35% ~18s
|+++++++++++++++++++ | 37% ~17s
|+++++++++++++++++++ | 38% ~17s
|++++++++++++++++++++ | 39% ~17s
|++++++++++++++++++++ | 40% ~17s
|+++++++++++++++++++++ | 41% ~16s
|+++++++++++++++++++++ | 42% ~16s
|++++++++++++++++++++++ | 43% ~16s
|+++++++++++++++++++++++ | 44% ~16s
|+++++++++++++++++++++++ | 45% ~15s
|++++++++++++++++++++++++ | 46% ~15s
|++++++++++++++++++++++++ | 47% ~15s
|+++++++++++++++++++++++++ | 48% ~14s
|+++++++++++++++++++++++++ | 49% ~14s
|++++++++++++++++++++++++++ | 51% ~14s
|++++++++++++++++++++++++++ | 52% ~13s
|+++++++++++++++++++++++++++ | 53% ~13s
|+++++++++++++++++++++++++++ | 54% ~13s
|++++++++++++++++++++++++++++ | 55% ~13s
|++++++++++++++++++++++++++++ | 56% ~12s
|+++++++++++++++++++++++++++++ | 57% ~12s
|++++++++++++++++++++++++++++++ | 58% ~12s
|++++++++++++++++++++++++++++++ | 59% ~11s
|+++++++++++++++++++++++++++++++ | 60% ~11s
|+++++++++++++++++++++++++++++++ | 61% ~11s
|++++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 63% ~10s
|+++++++++++++++++++++++++++++++++ | 65% ~10s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|++++++++++++++++++++++++++++++++++ | 68% ~09s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|+++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~08s
|+++++++++++++++++++++++++++++++++++++ | 72% ~08s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|++++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=27s
[1] "cluster 18 has 0 genes"
Error in WhichCells.Seurat(object = object, idents = ident.1) :
Cannot find the following identities in the object: 18
Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
var.df <- cmp.top100markers.res1[cmp.top100markers.res1$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster, value.var = "avg_log2FC")
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1", append = TRUE, row.names = FALSE)
>>>>>>> cmp
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Scale data (linear transformation)
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## Save raw object
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc2F2ZVJEUyhjbXAub2JqZWN0LCBmaWxlID0gcGFzdGUwKHByb2plY3ROYW1lLCBcXF9yYXcuUkRTXFwpKVxuYGBgXG5gYGAifQ== -->
```r
```r
saveRDS(cmp.object, file = paste0(projectName, \_raw.RDS\))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY21wLm9iamVjdCA8LSBSdW5QQ0EoY21wLm9iamVjdCwgZmVhdHVyZXMgPSBWYXJpYWJsZUZlYXR1cmVzKGNtcC5vYmplY3QpLCBuZGltcy5wcmludCA9IDE6NSwgbmZlYXR1cmVzLnByaW50ID0gNSlcbmBgYFxuYGBgIn0= -->
```r
```r
cmp.object <- RunPCA(cmp.object, features = VariableFeatures(cmp.object), ndims.print = 1:5, nfeatures.print = 5)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuRGltUGxvdChjbXAub2JqZWN0LCByZWR1Y3Rpb24gPSBcXHBjYVxcLCBncm91cC5ieSA9IFxcb3JpZy5pZGVudFxcKVxuVml6RGltTG9hZGluZ3MoY21wLm9iamVjdCwgZGltcyA9IDE6NiwgbmZlYXR1cmVzID0gMTAsIHJlZHVjdGlvbiA9IFxccGNhXFwsIG5jb2wgPSAzKVxuXG5gYGBcbmBgYCJ9 -->
```r
```r
DimPlot(cmp.object, reduction = \pca\, group.by = \orig.ident\)
VizDimLoadings(cmp.object, dims = 1:6, nfeatures = 10, reduction = \pca\, ncol = 3)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Calculate dimensionality
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRWxib3dQbG90KGNtcC5vYmplY3QsIG5kaW1zID0gNTApXG5wZXJjZW50LnZhcmlhbmNlKGNtcC5vYmplY3RAcmVkdWN0aW9ucyRwY2FAc3RkZXYpXG5gYGAifQ== -->
```r
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)
<<<<<<< HEAD
Number of PCs describing X% of variance
```r
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
# Add cluster IDs from Seurat v1
Exported cell IDs for clusters 3, 17, 10, 11 from Seurat v1. Will add these IDs as a metadata column.
Create column "clust.ID" and populate with 0's. Then import IDs for clusters
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0(\Num pcs for 80% variance:\, length(which(cumsum(tot.var) <= 80)))
paste0(\Num pcs for 85% variance:\, length(which(cumsum(tot.var) <= 85)))
paste0(\Num pcs for 90% variance:\, length(which(cumsum(tot.var) <= 90)))
paste0(\Num pcs for 95% variance:\, length(which(cumsum(tot.var) <= 95)))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Add new metadata column
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
clust3.cells <- read.table(file = \Seuratv1_clusterCellIDs/cluster3cellIDs.txt\, col.names = \clust03\)
clust3.cells <- sapply(clust3.cells, function(x) paste0(gsub(\CMP\, \CMPm2\, x), \-1\))
clust17.cells <- read.table(file = \Seuratv1_clusterCellIDs/cluster17cellIDs.txt\, col.names = \clust17\)
clust17.cells <- sapply(clust17.cells, function(x) paste0(gsub(\CMP\, \CMPm2\, x), \-1\))
clust10.cells <- read.table(file = \Seuratv1_clusterCellIDs/cluster10cellIDs.txt\, col.names = \clust10\)
clust10.cells <- sapply(clust10.cells, function(x) paste0(gsub(\CMP\, \CMPm2\, x), \-1\))
clust11.cells <- read.table(file = \Seuratv1_clusterCellIDs/cluster11cellIDs.txt\, col.names = \clust11\)
clust11.cells <- sapply(clust11.cells, function(x) paste0(gsub(\CMP\, \CMPm2\, x), \-1\))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
now map new ids
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY21wLm9iamVjdEBtZXRhLmRhdGFbJ2NsdXN0LklEJ10gPC0gMFxuaGVhZChjbXAub2JqZWN0QG1ldGEuZGF0YSlcbmBgYFxuYGBgIn0= -->
```r
```r
cmp.object@meta.data['clust.ID'] <- 0
head(cmp.object@meta.data)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
do numbers make sense?
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust3.cells] <- 3
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust17.cells] <- 17
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust10.cells] <- 10
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust11.cells] <- 11
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Color palette
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubnJvdyhjbXAub2JqZWN0QG1ldGEuZGF0YVtjbXAub2JqZWN0QG1ldGEuZGF0YSRjbHVzdC5JRCA9PSAxMCxdKVxubnJvdyhjbXAub2JqZWN0QG1ldGEuZGF0YVtjbXAub2JqZWN0QG1ldGEuZGF0YSRjbHVzdC5JRCA9PSAxMSxdKVxubnJvdyhjbXAub2JqZWN0QG1ldGEuZGF0YVtjbXAub2JqZWN0QG1ldGEuZGF0YSRjbHVzdC5JRCA9PSAxNyxdKVxubnJvdyhjbXAub2JqZWN0QG1ldGEuZGF0YVtjbXAub2JqZWN0QG1ldGEuZGF0YSRjbHVzdC5JRCA9PSAzLF0pXG5gYGBcbmBgYCJ9 -->
```r
```r
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 10,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 11,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 17,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 3,])
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
# Total var 90%
## Neighborhood and umap
set total.var <- 90%
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
color.palette <- c(
\coral\,
\chartreuse4\,
\goldenrod1\,
\cadetblue1\,
\burlywood\,
\brown\,
\brown1\,
\blue\,
\blue4\,
\azure3\,
\aquamarine\,
\antiquewhite\,
\cadetblue\,
\gold3\,
\black\,
\darkgreen\,
\deeppink\,
\darkviolet\,
\darkturquoise\,
\darkslategray\,
\darksalmon\,
\darkorchid1\,
\darkolivegreen2\,
\forestgreen\,
\dodgerblue\,
\green\,
\lightpink\,
\lightcoral\,
\khaki1\,
\maroon\,
\peru\,
\lightseagreen\,
\lightsalmon\,
\plum\,
\moccasin\,
\tan\,
\tan1\,
\red\,
\purple\,
\khaki4\,
\black\,
\plum4\
)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Plot UMAP
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))
=======
Here’s the list of genes, plus some controls
res1.biomark.genes <- c("Aif1",
"Aqp1",
"Birc5",
"Ccl3",
"Ccr2",
"Cd74",
"Cdc20",
"Cenpf",
"Cst3",
"Ctsh",
"Dntt",
"Elane",
"Ermap",
"F13a1",
"Fcer1g",
"Gm15915",
"Gm17590",
"H2-Aa",
"H2-Eb1",
"H2afx",
"H2afy",
"Hist1h2ac",
"Hmgb2",
"Hp",
"Ighm",
"Irf8",
"Lgals3",
"Lmo4",
"Ms4a2",
"Mt1",
"Plac8",
"Prtn3",
"Rap1b",
"Rgs1",
"Vwf",
"Wfdc17",
"Csrp3",
"Hist1h2ae",
"Ifitm1",
"Lgals1",
"Tmsb4x",
"Arl6ip1",
"Car2",
"Ccl9",
"Ccnb2",
"Cd9",
"Cenpa",
"Cpa3",
"Fos",
"Hist1h2ap",
"Ly6c2",
"Mpo",
"Pclaf",
"Slpi",
"Top2a",
"Ube2c",
"Ly86",
"Hist1h2bc",
"Pf4",
"Apoe",
"Ctsg",
"Car1",
"Hmmr")
hkgenes <- c("Gapdh", "B2m", "Hprt", "Pgk1", "Rplp2", "Pgk1", "Ubc", "Ywhaz", "Ppia", "Pum1", "Psmc4", "Elf1", "Mrpl19")
surface.markers<- c("Cd34",
"Kit",
"Cd48",
"Ly6a",
"Ly6e",
"Cd9",
"Itga2b",
"Ly86",
"Itga6",
"Cd55",
"Slamf1",
"Flt3"
)
make vln plots per cluster
for(gene in res1.biomark.genes){
plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
png(filename = paste0("VlnPlots/", plot.title, ".png"), height = 800, width = 1600)
plot(myplot)
dev.off()
}
for(gene in hkgenes){
plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
png(filename = paste0("VlnPlots/", plot.title, "-hkgene.png"), height = 800, width = 1600)
plot(myplot)
dev.off()
}
for(gene in surface.markers){
plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
png(filename = paste0("VlnPlots/", plot.title, "-surfMkr.png"), height = 800, width = 1600)
plot(myplot)
dev.off()
}
Plot candidate control genes
# VlnPlot(cmp.object, features = hkgenes, pt.size = 0.001, same.y.lims = FALSE, ncol = 2)
png(filename = paste0(projectName, "dim", ndims, "res1_TaqmanControls.png"), height = 1600, width = 2400)
VlnPlot(cmp.object, features = hkgenes, pt.size = 0.01, same.y.lims = FALSE, ncol = 5)
dev.off()
Res 1.5
repeat for top.var genes in cmp.top100markers.res1.5
top.var <- cmp.object@assays$RNA@var.features[1:150]
Idents(cmp.object) <- "RNA_snn_res.1.5"
cmp.allmarkers.res1.5 <- FindAllMarkers(cmp.object)
cmp.top100markers.res1.5 <- cmp.allmarkers.res1.5 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
n.vargenes <- c()
for(gene in top.var){
if(gene %in% cmp.top100markers.res1.5$gene){
n.vargenes <- c(n.vargenes, gene)
}
}
print(length(n.vargenes))
top.var[!(top.var %in% n.vargenes)]Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
var.df <- cmp.top100markers.res1.5[cmp.top100markers.res1.5$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster)
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1.5", append = TRUE, row.names = FALSE)
For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
print(enrich.df)
}
}
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
DimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)Gene expression of old clustrs on new map
Let’s see if we can get some gene expression profiles on these…
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))Total var 80%
Neighborhood and umap
set total.var <- 80%
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 80))
>>>>>>> cmp
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)
<<<<<<< HEAD
saveRDS(cmp.object, file = paste0(projectName, \_dim\, ndims, \.RDS\))
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```r
```r
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}
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for each resolution, number/percentage of cells in each cluster?
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```r
```r
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = (\RNA_snn_res\), x = meta.col)==TRUE){
=======
Plot UMAP
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
>>>>>>> cmp
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = \umap\,
cols = color.palette
) +
ggtitle(paste0(projectName, \ dim\, ndims, \res\, gsub(\RNA_snn_res\, \\, meta.col) ))
plot(myplot)
}
}
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<!-- rnb-text-begin -->
For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
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```r
```r
tot.cells <- nrow(cmp.object@meta.data)
=======
Clustree
what’s the max resolution we can achieve while keepign clusters stable?
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") +
scale_color_continuous(low = 'red3', high = 'white') +
ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()
for each resolution, number/percentage of cells in each cluster?
tot.cells <- nrow(cmp.object@meta.data)
>>>>>>> cmp
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = (\RNA_snn_res\), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
colnames(stats.df) <- c(\num_cells\, \pct_pop\)
rownames(stats.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(stats.df)){
num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
pct.x <- as.integer(num.x / tot.cells *100)
# print(pct.x)
stats.df[row.id, \num_cells\] <- num.x
stats.df[row.id, \pct_pop\] <- pct.x
}
print(stats.df)
}
}
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Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
## Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
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```r
```r
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = (\RNA_snn_res\), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0(\oldcluster\, x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0(\newcluster\, x))
xlsx::write.xlsx(enrich.df, file = paste0(\PctOfNewClustersOverlappingOldClusters_\, projectName, \_dim\, ndims, \.xlsx\), sheetName = paste0(gsub(\RNA_snn_\, \\, meta.col)), append = TRUE)
print(enrich.df)
}
}
<<<<<<< HEAD
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```r
```r
DimPlot(cmp.object,
reduction = \umap\,
group.by = \clust.ID\,
# split.by = \orig.ident\,
cols = c(\gray\, \orange\, \blue\, \red\, \green\),)
<!-- rnb-source-end -->
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# Total var 95%
## Neighborhood and umap
set total.var <- 95%
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```r
```r
DimPlot(cmp.object,
reduction = \umap\,
group.by = \orig.ident\,
split.by = \clust.ID\,
cols = c(\gray\, \orange\, \blue\, \red\, \green\),)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Plot UMAP
<!-- rnb-text-end -->
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```r
```r
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
=======
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
DimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)Total var 95%
Neighborhood and umap
set total.var <- 95%
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
>>>>>>> cmp
ndims <- length(which(cumsum(tot.var) <= 95))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)
<<<<<<< HEAD
saveRDS(cmp.object, file = paste0(projectName, \_dim\, ndims, \.RDS\))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
## Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
<!-- rnb-text-end -->
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```r
```r
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = (\RNA_snn_res\), x = meta.col)==TRUE){
=======
Plot UMAP
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))cmp.object <- readRDS("CMP_dim41.RDS")plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") +
scale_color_continuous(low = 'red3', high = 'white') +
ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
>>>>>>> cmp
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
<<<<<<< HEAD
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0(\oldcluster\, x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0(\newcluster\, x))
xlsx::write.xlsx(enrich.df, file = paste0(\PctOfNewClustersOverlappingOldClusters_\, projectName, \_dim\, ndims, \.xlsx\), sheetName = paste0(gsub(\RNA_snn_\, \\, meta.col)), append = TRUE)
=======
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
>>>>>>> cmp
print(enrich.df)
}
}
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<!-- rnb-chunk-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuRGltUGxvdChjbXAub2JqZWN0LFxuXHRcdFx0XHRyZWR1Y3Rpb24gPSBcXHVtYXBcXCxcblx0XHRcdFx0Z3JvdXAuYnkgPSBcXGNsdXN0LklEXFwsIFxuXHRcdFx0XHRwdC5zaXplID0gLjEsXG5cdFx0XHRcdCMgc3BsaXQuYnkgPSBcXG9yaWcuaWRlbnRcXCxcblx0XHRcdFx0Y29scyA9IGMoXFxncmF5XFwsIFxcb3JhbmdlXFwsIFxcYmx1ZVxcLCBcXHJlZFxcLCBcXGdyZWVuXFwpLClcbmBgYFxuYGBgIn0= -->
```r
```r
DimPlot(cmp.object,
reduction = \umap\,
group.by = \clust.ID\,
pt.size = .1,
# split.by = \orig.ident\,
cols = c(\gray\, \orange\, \blue\, \red\, \green\),)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Gene expression of old clustrs on new map
Let's see if we can get some gene expression profiles on these...
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
DimPlot(cmp.object,
reduction = \umap\,
group.by = \orig.ident\,
split.by = \clust.ID\,
cols = c(\gray\, \orange\, \blue\, \red\, \green\),)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Used the exce doc to do some fancy conditional formatting. Old cluster 17 is pretty dispersed until you it resolution 2.5. Otherise, cells in old cluster 17 do not constitute more than 40% of any cells in the new clusters.
As far as I can see, the two approaches are to do DGEof new CMP w/ resolution = 2.5, AND/OR do DGe using older cluster IDs. Sure seems to make sense to do both...
# DGE w/ resolution = 2.5
Strt with comparing all clusters against all other clusters
Write out cluster info
calculate `FindAllMarkers()` for different idents and save to new file
<!-- rnb-text-end -->
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```r
```r
gene.list <- c(\Gata1\, \Gata2\, \Pf4\, \Dntt\, \Mpo\, \Meis1\, \Irf8\, \Elane\, \Fli1\, \Zfpm1\)
VlnPlot(cmp.object, features = gene.list, group.by = \clust.ID\, pt.size = 0.01, cols = c(\gray\, \orange\, \blue\, \red\, \green\))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Create `FindAllMarkers()` lists for GSEA
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
ident.list <- c(\RNA_snn_res.0.5\, \RNA_snn_res.1\, \RNA_snn_res.1.5\, \RNA_snn_res.2\, \RNA_snn_res.2.5\, \clust.ID\)
=======
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
DimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
pt.size = .1,
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)Gene expression of old clustrs on new map
Let’s see if we can get some gene expression profiles on these…
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))Used the exce doc to do some fancy conditional formatting. Old cluster 17 is pretty dispersed until you it resolution 2.5. Otherise, cells in old cluster 17 do not constitute more than 40% of any cells in the new clusters.
As far as I can see, the two approaches are to do DGEof new CMP w/ resolution = 2.5, AND/OR do DGe using older cluster IDs. Sure seems to make sense to do both…
DGE w/ resolution = 2.5
Strt with comparing all clusters against all other clusters Write out cluster info
calculate FindAllMarkers() for different idents and save to new file
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
>>>>>>> cmp
for(tested.ident in ident.list){
Idents(cmp.object) <- tested.ident
all.markers <- FindAllMarkers(cmp.object)
xlsx::write.xlsx(x = all.markers[,c(\avg_log2FC\, \p_val_adj\, \cluster\, \gene\)],
file = paste0(projectName, \_FindALLMarkers_res2.5.xlsx\),
sheetName = tested.ident,
col.names = TRUE,
row.names = FALSE,
append = TRUE)
}
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Map HGNC symbols
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
Idents(cmp.object) <- \RNA_snn_res.2.5\
res.2.5.allmarkers <- FindAllMarkers(cmp.object)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
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```r
```r
Mouse2HumanTable <- Mouse2Human(res.2.5.allmarkers$gene)
=======
Map HGNC symbols
Mouse2HumanTable <- Mouse2Human(res.2.5.allmarkers$gene)
>>>>>>> cmp
HGNC <- with(Mouse2HumanTable, Mouse2HumanTable$HGNC[match(res.2.5.allmarkers$gene, Mouse2HumanTable$MGI)])
head(res.2.5.allmarkers)
res.2.5.allmarkers$HGNC <- HGNC
tail(res.2.5.allmarkers)
sig.res.2.5 <- res.2.5.allmarkers[res.2.5.allmarkers$p_val_adj <= 0.05, ]
sig.res.2.5 <- sig.res.2.5[c(\avg_log2FC\, \HGNC\, \cluster\)]
sig.res.2.5 <- sig.res.2.5[!(sig.res.2.5$HGNC == \\ | is.na(sig.res.2.5$HGNC)),] # GSEA will fail if there are any blanks or NAs in the table
sig.res.2.5 <- sig.res.2.5[]
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
calculate `FindMarkers()` that distinguish each cluster (might overlab between clusters)
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
for(cluster in unique(sig.res.2.5$cluster)){
print(paste(\writing cluster\, cluster))
new.table <- sig.res.2.5[sig.res.2.5$cluster == cluster, c(\HGNC\, \avg_log2FC\)]
new.table <- new.table[order(-new.table$avg_log2FC), ]
write.table(new.table, file = paste0(\RankList_res2.5_findAll_hgnc/res.2.5cluster\, cluster, \.rnk\), quote = FALSE, row.names = FALSE, col.names = TRUE, sep = \\t\, )
}
<<<<<<< HEAD
<!-- rnb-source-end -->
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```r
```r
ident.list <- c(\RNA_snn_res.0.5\, \RNA_snn_res.1\, \RNA_snn_res.1.5\, \RNA_snn_res.2\, \RNA_snn_res.2.5\, \clust.ID\)
for(tested.ident in ident.list){
for (cluster in sort(as.numeric(levels(cmp.object@meta.data[[tested.ident]])))){
cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
xlsx::write.xlsx(x = cluster.markers[,c(\avg_log2FC\, \p_val_adj\)],
file = paste0(projectName, \_FindMarkers_\, gsub(\RNA_snn_\, \\, tested.ident), \.xlsx\),
sheetName = paste0(\clst\, cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
}
=======
calculate FindMarkers() that distinguish each cluster (might overlab between clusters)
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
object.copy <- cmp.object
Idents(object.copy) <- tested.ident
print(paste("testing", tested.ident))
for (cluster in sort(as.numeric(levels(object.copy@meta.data[[tested.ident]])))){
print(paste("looking at cluster", cluster))
cluster.markers <- FindMarkers(object.copy, ident.1 = cluster)
try(
xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")],
file = paste0(projectName, "_FindMarkers_", gsub("RNA_snn_", "", tested.ident), ".xlsx"),
sheetName = paste0("clst", cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
)
}
remove(object.copy)
>>>>>>> cmp
}
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## Combine clusters that might represent old cluster ids
# DGE w/ metadata against clust.ID against "0"
reset ident as "clust.ID" and rerun `FindAllMarkers()`
<!-- rnb-text-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
for (cluster in sort(as.numeric(levels(cmp.object@meta.data$RNA_snn_res.2.5)))){
cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
xlsx::write.xlsx(x = cluster.markers[,c(\avg_log2FC\, \p_val_adj\)],
file = paste0(projectName, \_FindMarkers_res2.5.xlsx\),
sheetName = paste0(\clst\, cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
}
<<<<<<< HEAD
<!-- rnb-source-end -->
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```r
```r
Idents(cmp.object) <- \clust.ID\
=======
Combine clusters that might represent old cluster ids
DGE w/ metadata against clust.ID against “0”
reset ident as “clust.ID” and rerun FindAllMarkers()
Idents(cmp.object) <- "clust.ID"
>>>>>>> cmp
all.markers <- FindAllMarkers(cmp.object)
xlsx::write.xlsx(x = all.markers[,c(\avg_log2FC\, \p_val_adj\, \cluster\, \gene\)],
file = paste0(projectName, \_FindALLMarkers_clustID.xlsx\),
sheetName = \clustID\,
col.names = TRUE,
row.names = FALSE,
append = TRUE)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
# Distinguishing features of clusters
Previously defined biomark genes based on PC contributions. Original list was based on *all* msAggr, but let's see how CMP subset does?
<!-- rnb-text-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
# Idents(cmp.object) <- \clust.ID\
for (cluster in unique(cmp.object@meta.data$clust.ID)){
print(cluster)
cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
xlsx::write.xlsx(x = cluster.markers[,c(\avg_log2FC\, \p_val_adj\)],
file = paste0(projectName, \_FindMarkers_clustID.xlsx\),
sheetName = paste0(\oldclust\, cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
}
<!-- rnb-source-end -->
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<!-- rnb-text-begin -->
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```r
```r
VizDimLoadings(cmp.object, dims = 1:10, nfeatures = 30, reduction = \pca\, ncol = 2)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
now we can get a list of principal components!
first pull the list of oldAnalysis CMP top PC genes
<!-- rnb-text-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucGNhLmRmIDwtIGNtcC5vYmplY3RbW1xccGNhXFxdXVxucGNhLmRmIDwtIGFzLmRhdGEuZnJhbWUoYXMubWF0cml4KHNsb3Qob2JqZWN0ID0gcGNhLmRmLCBuYW1lID0gXFxmZWF0dXJlLmxvYWRpbmdzXFwpKSlcbnByaW50KGNtcC5vYmplY3RbW1xccGNhXFxdXSwgZGltcyA9IDIsIG5mZWF0dXJlcyA9IDUpXG5yb3duYW1lcyhwY2EuZGZbcGNhLmRmJFBDXzIgJWluJSBzb3J0KHBjYS5kZiRQQ18yLCBkZWNyZWFzaW5nID0gVFJVRSlbMTo1XSwgXSlcbnJvd25hbWVzKHBjYS5kZltwY2EuZGYkUENfMiAlaW4lIHNvcnQocGNhLmRmJFBDXzIpWzE6NV0sIF0pXG5gYGBcbmBgYCJ9 -->
```r
```r
pca.df <- cmp.object[[\pca\]]
pca.df <- as.data.frame(as.matrix(slot(object = pca.df, name = \feature.loadings\)))
print(cmp.object[[\pca\]], dims = 2, nfeatures = 5)
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2, decreasing = TRUE)[1:5], ])
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2)[1:5], ])
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Now get the list of current pc gene trgets (oldAnalysis used ndim = 1:6, so we'll start with that range)
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
cmp.biomark <- read.table(file = \/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/CMP_PCTopGenes.txt\, sep = \\t\, header = TRUE)
biomark.cmptargets <- c()
for(df.col in 1:ncol(cmp.biomark)){
biomark.cmptargets <- c(biomark.cmptargets, biomark[,df.col])
}
print(colnames(biomark))
print(paste(\total gene count:\, length(biomark.cmptargets)))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Now compare the lists, I guess:
<!-- rnb-text-end -->
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<!-- rnb-source-begin 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 -->
```r
```r
pc.list <- c(\PC_1\, \PC_2\, \PC_3\, \PC_4\, \PC_5\, \PC_6\)
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:30],])) #targeting roughly 180 genes like in biomark.cmptargets
pc.genes <- unique(unlist(pc.genes))
print(paste(\total gene count:\, length(pc.genes)))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Umm, yeah that went kinda how I expected. Let's do this again, but for the actual biomark gene lists.
<!-- rnb-text-end -->
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```r
```r
# setdiff(x,y) gives you things in x not in y. setdiff(y,x) gives you things in y not in x
setdiff(biomark.cmptargets, pc.genes)
# print(paste(\\n length:\, length(setdiff(biomark.cmptargets, pc.genes))))
writeLines(c(\\, \length:\, length(setdiff(biomark.cmptargets, pc.genes))))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
What if we increase the number of pcs but decrease the depth of each? This might cover more of `biomark `, which was originally developed using msAggr instead of only the CMP subset
<!-- rnb-text-end -->
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```r
```r
biomark <- read.table(file = \/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/BiomarkProbeList.txt\, sep = \\t\)
biomark <- biomark[,1]
setdiff(biomark, pc.genes)
writeLines(c(\\, \length:\, length(setdiff(biomark, pc.genes))))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucGMubGlzdCA8LSBjKFxcUENfMVxcLCBcXFBDXzJcXCwgXFxQQ18zXFwsIFxcUENfNFxcLCBcXFBDXzVcXCwgXFxQQ182XFwsIFxcUENfN1xcLCBcXFBDXzhcXCwgXFxQQ185XFwsIFxcUENfMTBcXClcbnBjLmdlbmVzIDwtIGxhcHBseShwYy5saXN0LCBmdW5jdGlvbih4KSByb3duYW1lcyhwY2EuZGZbcGNhLmRmW1t4XV0gJWluJSBzb3J0KHBjYS5kZltbeF1dLCBkZWNyZWFzaW5nID0gVFJVRSlbMToyMF0sXSkpXG5wYy5nZW5lcyA8LSB1bmlxdWUodW5saXN0KHBjLmdlbmVzKSlcbnByaW50KHBhc3RlKFxcdG90YWwgZ2VuZSBjb3VudDpcXCwgbGVuZ3RoKHBjLmdlbmVzKSkpXG5cbmBgYFxuYGBgIn0= -->
```r
```r
pc.list <- c(\PC_1\, \PC_2\, \PC_3\, \PC_4\, \PC_5\, \PC_6\, \PC_7\, \PC_8\, \PC_9\, \PC_10\)
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:20],]))
pc.genes <- unique(unlist(pc.genes))
print(paste(\total gene count:\, length(pc.genes)))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
For comparison, let's just see how many of `biomark.cmptargets` were actually included in `biomark`
<!-- rnb-text-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc2V0ZGlmZihiaW9tYXJrLCBwYy5nZW5lcylcbndyaXRlTGluZXMoYyhcXFxcLCBcXGxlbmd0aDpcXCwgbGVuZ3RoKHNldGRpZmYoYmlvbWFyaywgcGMuZ2VuZXMpKSkpXG5gYGBcbmBgYCJ9 -->
```r
```r
setdiff(biomark, pc.genes)
writeLines(c(\\, \length:\, length(setdiff(biomark, pc.genes))))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc2V0ZGlmZihiaW9tYXJrLmNtcHRhcmdldHMsIGJpb21hcmspXG53cml0ZUxpbmVzKGMoXFxcXCwgXFxsZW5ndGg6XFwsIGxlbmd0aChzZXRkaWZmKGJpb21hcmsuY21wdGFyZ2V0cywgcGMuZ2VuZXMpKSkpXG5gYGBcbmBgYCJ9 -->
```r
```r
setdiff(biomark.cmptargets, biomark)
writeLines(c(\\, \length:\, length(setdiff(biomark.cmptargets, pc.genes))))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGVuZ3RoKGJpb21hcmspIC0gbGVuZ3RoKHNldGRpZmYoYmlvbWFyaywgYmlvbWFyay5jbXB0YXJnZXRzKSlcbmBgYFxuYGBgIn0= -->
```r
```r
length(biomark) - length(setdiff(biomark, biomark.cmptargets))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
So when you look at it like that, it's not actually that far off.
What are the similarities?:
<!-- rnb-text-end -->
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```r
```r
length(biomark) - length(setdiff(biomark, pc.genes))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
These are genes from the 97probes not in the old CMP set that are also not in the new CMP set. Other than Itga2b (which is a failed probe anyway), nothing screams. Also we'd have thrown Flt3 and Cd34 for in anyway because they're requisite cell surface markers (also Flt3 surface marker is expensive but otherwise not noteworthy and not used in the current sorting strategy)
What about cell surface marker expression?
* Cd34
* Cd16/32
* Cd9
* Cd41
* Cd48
* Sca1 (just throw that in for sh*&s and giggles)
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc2V0ZGlmZihzZXRkaWZmKGJpb21hcmssIGJpb21hcmsuY21wdGFyZ2V0cyksIHNldGRpZmYoYmlvbWFyaywgcGMuZ2VuZXMpKVxuYGBgXG5gYGAifQ== -->
```r
```r
setdiff(setdiff(biomark, biomark.cmptargets), setdiff(biomark, pc.genes))
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Save as png
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
surface.markers <- c(\Cd34\, \Fcgr3\, \Fcgr2b\, \Cd9\, \Itga2b\, \Cd48\, \Ly6a\)
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = \clust.ID\, ncol = 1)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
# Cell cycle analysis
Just so we know what we're working with
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucG5nKGZpbGVuYW1lID0gXFxGZWF0dXJlUGxvdF9DTVBfc3VyZmFjZU1hcmtlcnNfY2x1c3RJRGZhY2V0LnBuZ1xcLCBoZWlnaHQgPSAxNjAwLCB3aWR0aCA9IDE2MDApXG5GZWF0dXJlUGxvdChjbXAub2JqZWN0LCBmZWF0dXJlcyA9IHN1cmZhY2UubWFya2VycywgcHQuc2l6ZSA9IDEsIHNwbGl0LmJ5ID0gXFxjbHVzdC5JRFxcLCBuY29sID0gMSlcbmRldi5vZmYoKVxuYGBgXG5gYGAifQ== -->
```r
```r
png(filename = \FeaturePlot_CMP_surfaceMarkers_clustIDfacet.png\, height = 1600, width = 1600)
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = \clust.ID\, ncol = 1)
dev.off()
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
# Compare @ hierarchcial clusteirng
Do clustering using biomark RNAs as input
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucy5nZW5lcyA8LSBjYy5nZW5lcy51cGRhdGVkLjIwMTkkcy5nZW5lc1xuZzJtLmdlbmVzIDwtIGNjLmdlbmVzLnVwZGF0ZWQuMjAxOSRnMm0uZ2VuZXNcbmBgYFxuYGBgIn0= -->
```r
```r
s.genes <- cc.genes.updated.2019$s.genes
g2m.genes <- cc.genes.updated.2019$g2m.genes
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
use biomark RNAs to define dimensional reduction
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyBSZWFkIGluIEJpb21hcmtSTkFzXG5iaW9tYXJrLnJuYXMgPC0gcmVhZC50YWJsZSgnL1VzZXJzL2hldXN0b25lZi9EZXNrdG9wLzEwWEdlbm9taWNzRGF0YS9CaW9tYXJrUk5Bcy50eHQnKVxuYmlvbWFyay5ybmFzIDwtIGJpb21hcmsucm5hcyRWMVxuYGBgXG5gYGAifQ== -->
```r
```r
# Read in BiomarkRNAs
=======
Compare @g hierarchcial clusteirng
Do clustering using biomark RNAs as input
# Read in BiomarkRNAs
>>>>>>> cmp
biomark.rnas <- read.table('/Users/heustonef/Desktop/10XGenomicsData/BiomarkRNAs.txt')
biomark.rnas <- biomark.rnas$V1
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Now run the clustering
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
cmp.object <- readRDS(\CMP_raw.RDS\)
cmp.object <- RunPCA(cmp.object, features = biomark.rnas, ndims.print = 1:5, , nfeatures.print = 5)
ElbowPlot(cmp.object, ndims = 50)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
find the clusters
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxudG90LnZhciA8LSBwZXJjZW50LnZhcmlhbmNlKGNtcC5vYmplY3RAcmVkdWN0aW9ucyRwY2FAc3RkZXYsIHBsb3QudmFyID0gRkFMU0UsIHJldHVybi52YWwgPSBUUlVFKVxubmRpbXMgPC0gbGVuZ3RoKHdoaWNoKGN1bXN1bSh0b3QudmFyKSA8PSA5MCkpXG5cbmNtcC5vYmplY3QgPC0gRmluZE5laWdoYm9ycyhjbXAub2JqZWN0LCBkaW1zID0gMTpuZGltcylcbmNtcC5vYmplY3QgPC0gRmluZENsdXN0ZXJzKGNtcC5vYmplY3QsIHJlc29sdXRpb24gPSAwLjUpXG5jbXAub2JqZWN0IDwtIFJ1blVNQVAoY21wLm9iamVjdCwgZGltcyA9IDE6IG5kaW1zKVxuXG5cbmBgYFxuYGBgIn0= -->
```r
```r
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
Plot the umaps and cell cluster ids
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Calculate anticipated number of cells you'll find in each biomark cluster
Get # cells in each cluster
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
```r
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = (\RNA_snn_res\), x = meta.col)==TRUE){
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = \umap\,
cols = color.palette
) +
ggtitle(paste0(projectName, \ dim\, ndims, \res\, gsub(\RNA_snn_res\, \\, meta.col) ))
plot(myplot)
}
}
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
So we did the dimensional reduction based on the biomark RNAs, then did our UMAP nearest neighbor clustering.
In the biomark hierarchcial clustering analysis I assayed 167 cells. The smallest cluster I detected had 3 cells, or 1.8% of total, and this is an uncomfortably small number of cells. Based on the UMAP calculations I would therefore expect to find 11 or 12 of the predicted 15 clusters. I found 12, and I don't really like that last one, so 11 or 12. Since I did the hierarchcial clustering yesterday and did this math today, we can say it was independent of these results and therefore totally legit. Yay!!
# Export for Biomark clustering
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
```r
tot.cellcount <- nrow(cmp.object@meta.data)
res05.list <- sort(unique(cmp.object@meta.data$RNA_snn_res.0.5), decreasing = FALSE)
sapply(res05.list,
function(x){
print(
paste(
\cluster\, x, \=\,
nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,]),
\cells or\,
round(nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,])/tot.cellcount*100, digits = 2),
\% of total\
)
)
}
)
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-chunk-begin -->
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```r
seurat.xprsn <- seurat.object@assays$RNA@scale.data
seurat.xprsn <- seurat.xprsn[row.names(seurat.xprsn) %in% biomark.genes,]
seurat.xprsn <- merge(seurat.xprsn, seurat.object@meta.data$RNA_snn_res.1, by = rownames(seurat.xprsn))---
title: "CMPSubset"
output: html_notebook
---

# Updates

## Update 2021.08.11
Most thorough way to readdress differences between original and current analyses is to jsut redo the biomark. To that end, will restart analysis of *just* CMPm2, excluding other datasets (i.e., LSK, MEP, GMP). Will also keep 


# Notebook setup

Creating new pipeline using seurat v4.0.2 available 2021.06.08

Load libraries required for Seuratv4

```{r setup}
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
knitr::opts_knit$set(root.dir = "~/Desktop/10XGenomicsData/msAggr_scRNASeq/CMP/")
# library(clustree)
```

# Set global variables
```{r}
projectName <- "CMP"
jackstraw.dim <- 40
```


## Store session info
```{r}
sink(paste0(projectName, "_sessionInfo.txt"))
sessionInfo()
sink()
```


```{r}
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/read_10XGenomics_data.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/PercentVariance.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/Mouse2Human_idconversion.R")
```


```{r warning=FALSE}
setwd("~/Desktop/10XGenomicsData/cellRanger/") # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = "CMPm2")
object.data <-Read10X(data_file.list)
```



```{r}
cmp.object<- CreateSeuratObject(counts = object.data, min.cells = 3, min.genes = 200, project = projectName)
```

Clean up to free memory

```{r}
remove(object.data)
```


Add mitochondrial metadata and plot some basic features
```{r}
cmp.object[["percent.mt"]] <- PercentageFeatureSet(cmp.object, pattern = "^mt-")
VlnPlot(cmp.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
```


```{r}
plot1 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
```

We don't have to worry about comparing library depths, so we'll just do normalization/Scale data



remove low quality cells
require: nFeature_RNA between 200 and 4000 (inclusive)
require: percent.mt <=5

```{r}
print(paste("original object:", nrow(cmp.object@meta.data), "cells", sep = " "))
cmp.object <- subset(cmp.object, 
												subset = nFeature_RNA >=200 & 
													nFeature_RNA <= 4000 & 
													percent.mt <= 5
												)
print(paste("new object:", nrow(cmp.object@meta.data), "cells", sep = " "))
```



```{r}
cmp.object <- NormalizeData(cmp.object, normalization.method = "LogNormalize", scale.factor = 10000)
```


Find variable features
```{r fig.width = 5, fig.height = 2}
cmp.object <- FindVariableFeatures(cmp.object, selection.method = "vst", nfeatures = 2000)
top10 <- head(VariableFeatures(cmp.object), 10)
plot1 <- VariableFeaturePlot(cmp.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2

```

Scale data (linear transformation)

```{r echo = FALSE}
all.genes <- rownames(cmp.object)
cmp.object <- ScaleData(cmp.object, features = all.genes, vars.to.regress = c("nFeature_RNA", "nCount_RNA"))
```

## Save raw object
```{r}
saveRDS(cmp.object, file = paste0(projectName, "_raw.RDS"))
```



```{r}
cmp.object <- RunPCA(cmp.object, features = VariableFeatures(cmp.object), ndims.print = 1:5, nfeatures.print = 5)
```

```{r}
DimPlot(cmp.object, reduction = "pca", group.by = "orig.ident")
VizDimLoadings(cmp.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 3)

```

Calculate dimensionality
```{r, figures-side, fig.show='hold', out.width="50%"}
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)
```
Number of PCs describing X% of variance

```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance:", length(which(cumsum(tot.var) <= 80)))
paste0("Num pcs for 85% variance:", length(which(cumsum(tot.var) <= 85)))
paste0("Num pcs for 90% variance:", length(which(cumsum(tot.var) <= 90)))
paste0("Num pcs for 95% variance:", length(which(cumsum(tot.var) <= 95)))

```

# Add cluster IDs from Seurat v1

Exported cell IDs for clusters 3, 17, 10, 11 from Seurat v1. Will add these IDs as a metadata column.  
Create column "clust.ID" and populate with 0's. Then import IDs for clusters



```{r}
clust3.cells <- read.table(file = "Seuratv1_clusterCellIDs/cluster3cellIDs.txt", col.names = "clust03")
clust3.cells <- sapply(clust3.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust17.cells <- read.table(file = "Seuratv1_clusterCellIDs/cluster17cellIDs.txt", col.names = "clust17")
clust17.cells <- sapply(clust17.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust10.cells <- read.table(file = "Seuratv1_clusterCellIDs/cluster10cellIDs.txt", col.names = "clust10")
clust10.cells <- sapply(clust10.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust11.cells <- read.table(file = "Seuratv1_clusterCellIDs/cluster11cellIDs.txt", col.names = "clust11")
clust11.cells <- sapply(clust11.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
```

Add new metadata column
```{r}
cmp.object@meta.data['clust.ID'] <- 0
head(cmp.object@meta.data)
```

now map new ids
```{r}
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust3.cells] <- 3
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust17.cells] <- 17
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust10.cells] <- 10
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust11.cells] <- 11
```

do numbers make sense?
```{r}
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 10,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 11,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 17,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 3,])
```

### Color palette
```{r}
color.palette <- c(
	"coral",
	"chartreuse4",
	"goldenrod1",
	"cadetblue1",
	"burlywood",
	"brown",
	"brown1",
	"blue",
	"blue4",
	"azure3",
	"aquamarine",
	"antiquewhite",
	"cadetblue",
	"gold3",
	"black",
	"darkgreen",
	"deeppink",
	"darkviolet",
	"darkturquoise",
	"darkslategray",
	"darksalmon",
	"darkorchid1",
	"darkolivegreen2",
	"forestgreen",
	"dodgerblue",
	"green",
	"lightpink",
	"lightcoral",
	"khaki1",
	"maroon",
	"peru",
	"lightseagreen",
	"lightsalmon",
	"plum",
	"moccasin",
	"tan",
	"tan1", 
	"red", 
	"purple",
	"khaki4",
	"black", 
	"plum4"
)
```

# Total var 90%
## Neighborhood and umap
set total.var <- 90%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
```

```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```


for each resolution, number/percentage of cells in each cluster?

```{r}
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
		colnames(stats.df) <- c("num_cells", "pct_pop")
		rownames(stats.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(stats.df)){
				num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
				pct.x <- as.integer(num.x / tot.cells *100)
				# print(pct.x)
				stats.df[row.id, "num_cells"] <- num.x
				stats.df[row.id, "pct_pop"] <- pct.x
		}
		print(stats.df)
	}
}
```



For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```


# Total var 95%
## Neighborhood and umap
set total.var <- 95%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 95))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
```



For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 2}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				pt.size = .1,
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```



### Gene expression of old clustrs on new map
Let's see if we can get some gene expression profiles on these...
```{r, fig.height=10, fig.width=18}
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))
```


Used the exce doc to do some fancy conditional formatting. Old cluster 17 is pretty dispersed until you it resolution 2.5. Otherise, cells in old cluster 17 do not constitute more than 40% of any cells in the new clusters.  
As far as I can see, the two approaches are to do DGEof new CMP w/ resolution = 2.5, AND/OR do DGe using older cluster IDs. Sure seems to make sense to do both...


# DGE w/ resolution = 2.5
Strt with comparing all clusters against all other clusters
Write out cluster info


calculate `FindAllMarkers()` for different idents and save to new file
```{r}
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
	Idents(cmp.object) <- tested.ident
	all.markers <- FindAllMarkers(cmp.object)
	xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")], 
									 file = paste0(projectName, "_FindALLMarkers_res2.5.xlsx"), 
									 sheetName = tested.ident, 
									 col.names = TRUE, 
									 row.names = FALSE, 
									 append = TRUE)
}
```

Create `FindAllMarkers()` lists for GSEA
```{r}
Idents(cmp.object) <- "RNA_snn_res.2.5"
res.2.5.allmarkers <- FindAllMarkers(cmp.object)
```

Map HGNC symbols
```{r}
Mouse2HumanTable <- Mouse2Human(res.2.5.allmarkers$gene)

HGNC <- with(Mouse2HumanTable, Mouse2HumanTable$HGNC[match(res.2.5.allmarkers$gene, Mouse2HumanTable$MGI)])
head(res.2.5.allmarkers)
res.2.5.allmarkers$HGNC <- HGNC
tail(res.2.5.allmarkers)
sig.res.2.5 <- res.2.5.allmarkers[res.2.5.allmarkers$p_val_adj <= 0.05, ]
sig.res.2.5 <- sig.res.2.5[c("avg_log2FC", "HGNC", "cluster")]
sig.res.2.5 <- sig.res.2.5[!(sig.res.2.5$HGNC == "" | is.na(sig.res.2.5$HGNC)),] # GSEA will fail if there are any blanks or NAs in the table
sig.res.2.5 <- sig.res.2.5[]

```


```{r}
for(cluster in unique(sig.res.2.5$cluster)){
	print(paste("writing cluster", cluster))
	new.table <- sig.res.2.5[sig.res.2.5$cluster == cluster, c("HGNC", "avg_log2FC")]
	new.table <- new.table[order(-new.table$avg_log2FC), ]
	write.table(new.table, file = paste0("RankList_res2.5_findAll_hgnc/res.2.5cluster", cluster, ".rnk"), quote = FALSE, row.names = FALSE, col.names = TRUE, sep = "\t", )
	
}
```



calculate `FindMarkers()` that distinguish each cluster (might overlab between clusters)
```{r}
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
	for (cluster in sort(as.numeric(levels(cmp.object@meta.data[[tested.ident]])))){
	cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_", gsub("RNA_snn_", "", tested.ident), ".xlsx"), 
									 sheetName = paste0("clst", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}
}
```



```{r}
for (cluster in sort(as.numeric(levels(cmp.object@meta.data$RNA_snn_res.2.5)))){
	cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_res2.5.xlsx"), 
									 sheetName = paste0("clst", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}
```

## Combine clusters that might represent old cluster ids

# DGE w/ metadata against clust.ID against "0"
reset ident as "clust.ID" and rerun `FindAllMarkers()`
```{r}
	Idents(cmp.object) <- "clust.ID"
	all.markers <- FindAllMarkers(cmp.object)
	xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")], 
									 file = paste0(projectName, "_FindALLMarkers_clustID.xlsx"), 
									 sheetName = "clustID", 
									 col.names = TRUE, 
									 row.names = FALSE, 
									 append = TRUE)
```


```{r}
# Idents(cmp.object) <- "clust.ID"
for (cluster in unique(cmp.object@meta.data$clust.ID)){
	print(cluster)
	cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_clustID.xlsx"), 
									 sheetName = paste0("oldclust", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}

```


# Distinguishing features of clusters
Previously defined biomark genes based on PC contributions. Original list was based on *all* msAggr, but let's see how CMP subset does?
```{r fig.height = 30, fig.width=6}
VizDimLoadings(cmp.object, dims = 1:10, nfeatures = 30, reduction = "pca", ncol = 2)
```

```{r}
pca.df <- cmp.object[["pca"]]
pca.df <- as.data.frame(as.matrix(slot(object = pca.df, name = "feature.loadings")))
print(cmp.object[["pca"]], dims = 2, nfeatures = 5)
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2, decreasing = TRUE)[1:5], ])
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2)[1:5], ])
```

now we can get a list of principal components!  
first pull the list of oldAnalysis CMP top PC genes
```{r}
cmp.biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/CMP_PCTopGenes.txt", sep = "\t", header = TRUE)
biomark.cmptargets <- c()
for(df.col in 1:ncol(cmp.biomark)){
	biomark.cmptargets <- c(biomark.cmptargets, biomark[,df.col])
}
print(colnames(biomark))
print(paste("total gene count:", length(biomark.cmptargets)))
```

Now get the list of current pc gene trgets (oldAnalysis used ndim = 1:6, so we'll start with that range)
```{r}
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:30],])) #targeting roughly 180 genes like in biomark.cmptargets
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))
```

Now compare the lists, I guess:

```{r}
# setdiff(x,y) gives you things in x not in y. setdiff(y,x) gives you things in y not in x
setdiff(biomark.cmptargets, pc.genes)
# print(paste("\n length:", length(setdiff(biomark.cmptargets, pc.genes))))
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))
```
Umm, yeah that went kinda how I expected. Let's do this again, but for the actual biomark gene lists.
```{r}
biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/BiomarkProbeList.txt", sep = "\t")
biomark <- biomark[,1]
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))
```


What if we increase the number of pcs but decrease the depth of each? This might cover more of `biomark	`, which was originally developed using msAggr instead of only the CMP subset
```{r}
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6", "PC_7", "PC_8", "PC_9", "PC_10")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:20],]))
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))

```
```{r}
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))
```

For comparison, let's just see how many of `biomark.cmptargets` were actually included in `biomark`
```{r}
setdiff(biomark.cmptargets, biomark)
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))
```
```{r}
length(biomark) - length(setdiff(biomark, biomark.cmptargets))
```
```{r}
length(biomark) - length(setdiff(biomark, pc.genes))
```
So when you look at it like that, it's not actually that far off.


What are the similarities?:
```{r}
setdiff(setdiff(biomark, biomark.cmptargets), setdiff(biomark, pc.genes))
```
These are genes from the 97probes not in the old CMP set that are also not in the new CMP set. Other than Itga2b (which is a failed probe anyway), nothing screams. Also we'd have thrown Flt3 and Cd34 for in anyway because they're requisite cell surface markers (also Flt3 surface marker is expensive but otherwise not noteworthy and not used in the current sorting strategy)

What about cell surface marker expression?
* Cd34
* Cd16/32
* Cd9
* Cd41
* Cd48
* Sca1 (just throw that in for sh*&s and giggles)
```{r, fig.height = 15, fig.width=10}
surface.markers <- c("Cd34", "Fcgr3", "Fcgr2b", "Cd9", "Itga2b", "Cd48", "Ly6a")
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)
```
Save as png
```{r}
png(filename = "FeaturePlot_CMP_surfaceMarkers_clustIDfacet.png", height = 1600, width = 1600)
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)
dev.off()
```





# Cell cycle analysis
Just so we know what we're working with


```{r}
s.genes <- cc.genes.updated.2019$s.genes
g2m.genes <- cc.genes.updated.2019$g2m.genes
```


# Compare @ hierarchcial clusteirng

Do clustering using biomark RNAs as input
```{r}
# Read in BiomarkRNAs
biomark.rnas <- read.table('/Users/heustonef/Desktop/10XGenomicsData/BiomarkRNAs.txt')
biomark.rnas <- biomark.rnas$V1
```

use biomark RNAs to define dimensional reduction
```{r}
cmp.object <- readRDS("CMP_raw.RDS")
cmp.object <- RunPCA(cmp.object, features = biomark.rnas, ndims.print = 1:5, , nfeatures.print = 5)
ElbowPlot(cmp.object, ndims = 50)
```


Now run the clustering
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)


```

find the clusters

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
```

Plot the umaps and cell cluster ids
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```
### Calculate anticipated number of cells you'll find in each biomark cluster
Get # cells in each cluster

```{r}
tot.cellcount <- nrow(cmp.object@meta.data)
res05.list <- sort(unique(cmp.object@meta.data$RNA_snn_res.0.5), decreasing = FALSE)
sapply(res05.list, 
			 function(x){
			 	print(
			 		paste(
			 			"cluster", x, "=", 
			 			nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,]), 
			 			"cells or", 
			 			round(nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,])/tot.cellcount*100, digits = 2), 
			 			"% of total"
			 		)
			 	)
			 }
			)
```

So we did the dimensional reduction based on the biomark RNAs, then did our UMAP nearest neighbor clustering.


In the biomark hierarchcial clustering analysis I assayed 167 cells. The smallest cluster I detected had 3 cells, or 1.8% of total, and this is an uncomfortably small number of cells. Based on the UMAP calculations I would therefore expect to find 11 or 12 of the predicted 15 clusters. I found 12, and I don't really like that last one, so 11 or 12. Since I did the hierarchcial clustering yesterday and did this math today, we can say it was independent of these results and therefore totally legit. Yay!!

# Export for Biomark clustering

```{r}
biomark.genes <- read.table(file = "/Users/heustonef/Desktop/10XGenomicsData/MergeBiomarkWith10X/geneIDs.txt", header = FALSE)
biomark.genes <- unlist(biomark.genes)
```
```{r}
seurat.xprsn <- seurat.object@assays$RNA@scale.data
seurat.xprsn <- seurat.xprsn[row.names(seurat.xprsn) %in% biomark.genes,]
seurat.xprsn <- merge(seurat.xprsn, seurat.object@meta.data$RNA_snn_res.1, by = rownames(seurat.xprsn))
```


=======
---
title: "CMPSubset"
output: html_notebook
---

# Updates

## Update 2021.08.19
Pursuing Dim25 (85% of variance explained) res1 and res1.5 for biomark targets

## Update 2021.08.11
Most thorough way to readdress differences between original and current analyses is to jsut redo the biomark. To that end, will restart analysis of *just* CMPm2, excluding other datasets (i.e., LSK, MEP, GMP). Will also keep 


# Notebook setup

Creating new pipeline using seurat v4.0.2 available 2021.06.08

Load libraries required for Seuratv4

## Load libraries
```{r setup}
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(clustree)
```

## Set global variables
```{r}
projectName <- "CMP"
jackstraw.dim <- 40
```

## Store session info
```{r}
sessionInfo.filename <- paste0(projectName, "_sessionInfo.txt")
sink(sessionInfo.filename)
sessionInfo()
sink()
```

## Load local scripts
```{r}
source("../RFunctions/read_10XGenomics_data.R")
source("../RFunctions/PercentVariance.R")
source("../RFunctions/Mouse2Human_idconversion.R")
source ("../RFunctions/ColorPalette.R")
```

# Read CMPm2
```{r warning=FALSE}
setwd("../../cellRanger/") # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = "CMPm2")
object.data <-Read10X(data_file.list)
```



```{r}
cmp.object<- CreateSeuratObject(counts = object.data, min.cells = 3, min.genes = 200, project = projectName)
```

Clean up to free memory

```{r}
remove(object.data)
```


Add mitochondrial metadata and plot some basic features
```{r}
cmp.object[["percent.mt"]] <- PercentageFeatureSet(cmp.object, pattern = "^mt-")
VlnPlot(cmp.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
```


```{r}
plot1 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
```
## Filter data
remove low quality cells
require: nFeature_RNA between 200 and 4000 (inclusive)
require: percent.mt <=5

```{r}
print(paste("original object:", nrow(cmp.object@meta.data), "cells", sep = " "))
cmp.object <- subset(cmp.object, 
												subset = nFeature_RNA >=200 & 
													nFeature_RNA <= 4000 & 
													percent.mt <= 5
												)
print(paste("new object:", nrow(cmp.object@meta.data), "cells", sep = " "))
```



```{r}
cmp.object <- NormalizeData(cmp.object, normalization.method = "LogNormalize", scale.factor = 10000)
```


Find variable features
```{r fig.width = 5, fig.height = 2}
cmp.object <- FindVariableFeatures(cmp.object, selection.method = "vst", nfeatures = 2000)
top10 <- head(VariableFeatures(cmp.object), 10)
plot1 <- VariableFeaturePlot(cmp.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
```

## Scale data (linear transformation)
We don't have to worry about comparing library depths, so we'll just do normalization/Scale data

```{r echo = FALSE}
all.genes <- rownames(cmp.object)
cmp.object <- ScaleData(cmp.object, features = all.genes, vars.to.regress = c("nFeature_RNA", "nCount_RNA"))
```

## Save raw object
```{r}
saveRDS(cmp.object, file = paste0(projectName, "_raw.RDS"))
```



```{r}
cmp.object <- RunPCA(cmp.object, features = VariableFeatures(cmp.object), ndims.print = 1:5, nfeatures.print = 5)
```

```{r}
DimPlot(cmp.object, reduction = "pca", group.by = "orig.ident")
VizDimLoadings(cmp.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 3)

```

Calculate dimensionality
```{r, figures-side, fig.show='hold', out.width="50%"}
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)
```
Number of PCs describing X% of variance

```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance:", length(which(cumsum(tot.var) <= 80)))
paste0("Num pcs for 85% variance:", length(which(cumsum(tot.var) <= 85)))
paste0("Num pcs for 90% variance:", length(which(cumsum(tot.var) <= 90)))
paste0("Num pcs for 95% variance:", length(which(cumsum(tot.var) <= 95)))

```

# Add cluster IDs from Seurat v1

Exported cell IDs for clusters 3, 17, 10, 11 from Seurat v1. Will add these IDs as a metadata column.  
Create column "clust.ID" and populate with 0's. Then import IDs for clusters



```{r}
clust3.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster3cellIDs.txt", col.names = "clust03")
clust3.cells <- sapply(clust3.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust17.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster17cellIDs.txt", col.names = "clust17")
clust17.cells <- sapply(clust17.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust10.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster10cellIDs.txt", col.names = "clust10")
clust10.cells <- sapply(clust10.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust11.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster11cellIDs.txt", col.names = "clust11")
clust11.cells <- sapply(clust11.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
```

Add new metadata column
```{r}
cmp.object@meta.data['clust.ID'] <- 0
head(cmp.object@meta.data)
```

now map new ids
```{r}
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust3.cells] <- 3
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust17.cells] <- 17
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust10.cells] <- 10
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust11.cells] <- 11
```

do numbers make sense?
```{r}
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 10,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 11,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 17,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 3,])
```








# Total var 90%
## Neighborhood and umap
set total.var <- 90%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```

### Clustree
what's the max resolution we can achieve while keepign clusters stable?
```{r fig.width = 10, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()

```
I think I'm liking res.1.0 from this. Although how much does this change if I use fewer PCs...



for each resolution, number/percentage of cells in each cluster?

```{r}
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
		colnames(stats.df) <- c("num_cells", "pct_pop")
		rownames(stats.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(stats.df)){
				num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
				pct.x <- as.integer(num.x / tot.cells *100)
				# print(pct.x)
				stats.df[row.id, "num_cells"] <- num.x
				stats.df[row.id, "pct_pop"] <- pct.x
		}
		print(stats.df)
	}
}
```



For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```


# Total var 85%
## Neighborhood and umap
set total.var <- 85%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 85))
```

```{r}
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)
```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}
```

```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		plot.title <- paste0(projectName, "dim", ndims, "res", gsub("RNA_snn_res", "", meta.col))
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(plot.title)
		plot(myplot)
		png(filename = paste0(plot.title, ".png"), height = 800, width = 800)
		plot(DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette, 
											pt.size = 1.5
											) + 
			ggtitle(plot.title)
			)
		dev.off()
	}
}
```

### Clustree
what's the max resolution we can achieve while keepign clusters stable?
```{r fig.width = 15, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
plot(my.clustree)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()
```



for each resolution, number/percentage of cells in each cluster?

```{r}
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
		colnames(stats.df) <- c("num_cells", "pct_pop")
		rownames(stats.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(stats.df)){
				num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
				pct.x <- as.integer(num.x / tot.cells *100)
				# print(pct.x)
				stats.df[row.id, "num_cells"] <- num.x
				stats.df[row.id, "pct_pop"] <- pct.x
		}
		print(stats.df)
	}
}
```

## Identify variable genes for new biomark
```{r}
length(cmp.object@assays$RNA@var.features)
```





### Gene profiles of clusters
set ident at res = 1 and get markers
```{r}
Idents(cmp.object) <- "RNA_snn_res.1"
```

```{r}
cmp.allmarkers.res1 <- FindAllMarkers(cmp.object)
cmp.top100markers.res1 <- cmp.allmarkers.res1 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
```

```{r}
cmp.top100markers.res1 <- cmp.allmarkers.res1 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
cmp.top100markers.res1 <- cmp.top100markers.res1[cmp.top100markers.res1$p_val_adj <= 0.05, ]
for(cluster in sort(as.numeric(unique(cmp.top100markers.res1$cluster)))){
	num.hits <- nrow(cmp.top100markers.res1[cmp.top100markers.res1$cluster == cluster, ])
	print(paste("cluster", cluster, "has", num.hits, "genes"))
		cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
		try(
			xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
											 file = paste0(projectName, "_dim", ndims, "_FindMarkersTop100.xlsx"), 
											 sheetName = paste0("clst", cluster), 
											 col.names = TRUE, 
											 row.names = TRUE, 
											 append = TRUE)
		)	
	}
```


Find top.var	genes in cmp.top100markers.res1
```{r}
top.var <- cmp.object@assays$RNA@var.features[1:150]
# top.var
n.vargenes <- c()
for(gene in top.var){
	if(gene %in% cmp.top100markers.res1$gene){
		n.vargenes <- c(n.vargenes, gene)
	}
}
print(length(n.vargenes))
top.var[!(top.var %in% n.vargenes)]
```


Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
```{r}
var.df <- cmp.top100markers.res1[cmp.top100markers.res1$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster, value.var = "avg_log2FC")
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1", append = TRUE, row.names = FALSE)

```

Here's the list of genes, plus some controls
```{r}
res1.biomark.genes <- c("Aif1",
												"Aqp1",
												"Birc5",
												"Ccl3",
												"Ccr2",
												"Cd74",
												"Cdc20",
												"Cenpf",
												"Cst3",
												"Ctsh",
												"Dntt",
												"Elane",
												"Ermap",
												"F13a1",
												"Fcer1g",
												"Gm15915",
												"Gm17590",
												"H2-Aa",
												"H2-Eb1",
												"H2afx",
												"H2afy",
												"Hist1h2ac",
												"Hmgb2",
												"Hp",
												"Ighm",
												"Irf8",
												"Lgals3",
												"Lmo4",
												"Ms4a2",
												"Mt1",
												"Plac8",
												"Prtn3",
												"Rap1b",
												"Rgs1",
												"Vwf",
												"Wfdc17",
												"Csrp3",
												"Hist1h2ae",
												"Ifitm1",
												"Lgals1",
												"Tmsb4x",
												"Arl6ip1",
												"Car2",
												"Ccl9",
												"Ccnb2",
												"Cd9",
												"Cenpa",
												"Cpa3",
												"Fos",
												"Hist1h2ap",
												"Ly6c2",
												"Mpo",
												"Pclaf",
												"Slpi",
												"Top2a",
												"Ube2c",
												"Ly86",
												"Hist1h2bc",
												"Pf4",
												"Apoe",
												"Ctsg",
												"Car1",
												"Hmmr")
hkgenes <- c("Gapdh", "B2m", "Hprt", "Pgk1", "Rplp2", "Pgk1", "Ubc", "Ywhaz", "Ppia", "Pum1", "Psmc4", "Elf1", "Mrpl19")
surface.markers<- c("Cd34", 
										"Kit",
										"Cd48", 
										"Ly6a",
										"Ly6e",
										"Cd9",
										"Itga2b",
										"Ly86",
										"Itga6",
										"Cd55",
										"Slamf1",
										"Flt3"
										)
```

make vln plots per cluster
```{r}
for(gene in res1.biomark.genes){
	plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
	myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
	png(filename = paste0("VlnPlots/", plot.title, ".png"), height = 800, width = 1600)
	plot(myplot)
	dev.off()
}
for(gene in hkgenes){
	plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
	myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
	png(filename = paste0("VlnPlots/", plot.title, "-hkgene.png"), height = 800, width = 1600)
	plot(myplot)
	dev.off()
}
for(gene in surface.markers){
	plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
	myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
	png(filename = paste0("VlnPlots/", plot.title, "-surfMkr.png"), height = 800, width = 1600)
	plot(myplot)
	dev.off()
}
```

Plot candidate control genes
```{r fig.height=20, fig.width=10}
# VlnPlot(cmp.object, features = hkgenes, pt.size = 0.001, same.y.lims = FALSE, ncol = 2)
png(filename = paste0(projectName, "dim", ndims, "res1_TaqmanControls.png"), height = 1600, width = 2400)
VlnPlot(cmp.object, features = hkgenes, pt.size = 0.01, same.y.lims = FALSE, ncol = 5)
dev.off()
```


## Res 1.5

repeat for top.var	genes in cmp.top100markers.res1.5
```{r}
top.var <- cmp.object@assays$RNA@var.features[1:150]
Idents(cmp.object) <- "RNA_snn_res.1.5"
cmp.allmarkers.res1.5 <- FindAllMarkers(cmp.object)
cmp.top100markers.res1.5 <- cmp.allmarkers.res1.5 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
n.vargenes <- c()
for(gene in top.var){
	if(gene %in% cmp.top100markers.res1.5$gene){
		n.vargenes <- c(n.vargenes, gene)
	}
}
print(length(n.vargenes))
top.var[!(top.var %in% n.vargenes)]
```


Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
```{r}
var.df <- cmp.top100markers.res1.5[cmp.top100markers.res1.5$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster)
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1.5", append = TRUE, row.names = FALSE)

```
For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```

### Gene expression of old clustrs on new map
Let's see if we can get some gene expression profiles on these...
```{r, fig.height=10, fig.width=18}
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))
```


# Total var 80%
## Neighborhood and umap
set total.var <- 80%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 80))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}
```

```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```

### Clustree
what's the max resolution we can achieve while keepign clusters stable?
```{r fig.width = 10, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()
```



for each resolution, number/percentage of cells in each cluster?

```{r}
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
		colnames(stats.df) <- c("num_cells", "pct_pop")
		rownames(stats.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(stats.df)){
				num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
				pct.x <- as.integer(num.x / tot.cells *100)
				# print(pct.x)
				stats.df[row.id, "num_cells"] <- num.x
				stats.df[row.id, "pct_pop"] <- pct.x
		}
		print(stats.df)
	}
}
```



For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```



# Total var 95%

## Neighborhood and umap
set total.var <- 95%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 95))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

```{r}
cmp.object <- readRDS("CMP_dim41.RDS")
```


```{r fig.width = 10, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()
```

For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 2}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				pt.size = .1,
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```



### Gene expression of old clustrs on new map
Let's see if we can get some gene expression profiles on these...
```{r, fig.height=10, fig.width=18}
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))
```


Used the exce doc to do some fancy conditional formatting. Old cluster 17 is pretty dispersed until you it resolution 2.5. Otherise, cells in old cluster 17 do not constitute more than 40% of any cells in the new clusters.  
As far as I can see, the two approaches are to do DGEof new CMP w/ resolution = 2.5, AND/OR do DGe using older cluster IDs. Sure seems to make sense to do both...


# DGE w/ resolution = 2.5
Strt with comparing all clusters against all other clusters
Write out cluster info


calculate `FindAllMarkers()` for different idents and save to new file
```{r}
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
	Idents(cmp.object) <- tested.ident
	all.markers <- FindAllMarkers(cmp.object)
	xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")], 
									 file = paste0(projectName, "_FindALLMarkers_res2.5.xlsx"), 
									 sheetName = tested.ident, 
									 col.names = TRUE, 
									 row.names = FALSE, 
									 append = TRUE)
}
```

Create `FindAllMarkers()` lists for GSEA
```{r }
Idents(cmp.object) <- "RNA_snn_res.2.5"
res.2.5.allmarkers <- FindAllMarkers(cmp.object)
```

## Map HGNC symbols
```{r}
Mouse2HumanTable <- Mouse2Human(res.2.5.allmarkers$gene)

HGNC <- with(Mouse2HumanTable, Mouse2HumanTable$HGNC[match(res.2.5.allmarkers$gene, Mouse2HumanTable$MGI)])
head(res.2.5.allmarkers)
res.2.5.allmarkers$HGNC <- HGNC
tail(res.2.5.allmarkers)
sig.res.2.5 <- res.2.5.allmarkers[res.2.5.allmarkers$p_val_adj <= 0.05, ]
sig.res.2.5 <- sig.res.2.5[c("avg_log2FC", "HGNC", "cluster")]
sig.res.2.5 <- sig.res.2.5[!(sig.res.2.5$HGNC == "" | is.na(sig.res.2.5$HGNC)),] # GSEA will fail if there are any blanks or NAs in the table
sig.res.2.5 <- sig.res.2.5[]

```


```{r}
for(cluster in unique(sig.res.2.5$cluster)){
	print(paste("writing cluster", cluster))
	new.table <- sig.res.2.5[sig.res.2.5$cluster == cluster, c("HGNC", "avg_log2FC")]
	new.table <- new.table[order(-new.table$avg_log2FC), ]
	write.table(new.table, file = paste0("RankList_res2.5_findAll_hgnc/res.2.5cluster", cluster, ".rnk"), quote = FALSE, row.names = FALSE, col.names = TRUE, sep = "\t", )
	
}
```



calculate `FindMarkers()` that distinguish each cluster (might overlab between clusters)
```{r}
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
	object.copy <- cmp.object
	Idents(object.copy) <- tested.ident
	print(paste("testing", tested.ident))
	for (cluster in sort(as.numeric(levels(object.copy@meta.data[[tested.ident]])))){
		print(paste("looking at cluster", cluster))
		cluster.markers <- FindMarkers(object.copy, ident.1 = cluster)
		try(
			xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
											 file = paste0(projectName, "_FindMarkers_", gsub("RNA_snn_", "", tested.ident), ".xlsx"), 
											 sheetName = paste0("clst", cluster), 
											 col.names = TRUE, 
											 row.names = TRUE, 
											 append = TRUE)
		)	
	}
	remove(object.copy)
}
```



```{r}
for (cluster in sort(as.numeric(levels(cmp.object@meta.data$RNA_snn_res.2.5)))){
	cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_res2.5.xlsx"), 
									 sheetName = paste0("clst", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}
```

## Combine clusters that might represent old cluster ids

# DGE w/ metadata against clust.ID against "0"
reset ident as "clust.ID" and rerun `FindAllMarkers()`
```{r}
	Idents(cmp.object) <- "clust.ID"
	all.markers <- FindAllMarkers(cmp.object)
	xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")], 
									 file = paste0(projectName, "_FindALLMarkers_clustID.xlsx"), 
									 sheetName = "clustID", 
									 col.names = TRUE, 
									 row.names = FALSE, 
									 append = TRUE)
```


```{r}
# Idents(cmp.object) <- "clust.ID"
for (cluster in unique(cmp.object@meta.data$clust.ID)){
	print(cluster)
	cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_clustID.xlsx"), 
									 sheetName = paste0("oldclust", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}

```


# Distinguishing features of clusters
Previously defined biomark genes based on PC contributions. Original list was based on *all* msAggr, but let's see how CMP subset does?
```{r fig.height = 30, fig.width=6}
VizDimLoadings(cmp.object, dims = 1:10, nfeatures = 30, reduction = "pca", ncol = 2)
```

```{r}
pca.df <- cmp.object[["pca"]]
pca.df <- as.data.frame(as.matrix(slot(object = pca.df, name = "feature.loadings")))
print(cmp.object[["pca"]], dims = 2, nfeatures = 5)
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2, decreasing = TRUE)[1:5], ])
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2)[1:5], ])
```

now we can get a list of principal components!  
first pull the list of oldAnalysis CMP top PC genes
```{r}
cmp.biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/CMP_PCTopGenes.txt", sep = "\t", header = TRUE)
biomark.cmptargets <- c()
for(df.col in 1:ncol(cmp.biomark)){
	biomark.cmptargets <- c(biomark.cmptargets, biomark[,df.col])
}
print(colnames(biomark))
print(paste("total gene count:", length(biomark.cmptargets)))
```

Now get the list of current pc gene trgets (oldAnalysis used ndim = 1:6, so we'll start with that range)
```{r}
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:30],])) #targeting roughly 180 genes like in biomark.cmptargets
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))
```

Now compare the lists, I guess:

```{r}
# setdiff(x,y) gives you things in x not in y. setdiff(y,x) gives you things in y not in x
setdiff(biomark.cmptargets, pc.genes)
# print(paste("\n length:", length(setdiff(biomark.cmptargets, pc.genes))))
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))
```
Umm, yeah that went kinda how I expected. Let's do this again, but for the actual biomark gene lists.
```{r}
biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/BiomarkProbeList.txt", sep = "\t")
biomark <- biomark[,1]
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))
```


What if we increase the number of pcs but decrease the depth of each? This might cover more of `biomark	`, which was originally developed using msAggr instead of only the CMP subset
```{r}
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6", "PC_7", "PC_8", "PC_9", "PC_10")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:20],]))
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))

```
```{r}
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))
```

For comparison, let's just see how many of `biomark.cmptargets` were actually included in `biomark`
```{r}
setdiff(biomark.cmptargets, biomark)
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))
```
```{r}
length(biomark) - length(setdiff(biomark, biomark.cmptargets))
```
```{r}
length(biomark) - length(setdiff(biomark, pc.genes))
```
So when you look at it like that, it's not actually that far off.


What are the similarities?:
```{r}
setdiff(setdiff(biomark, biomark.cmptargets), setdiff(biomark, pc.genes))
```
These are genes from the 97probes not in the old CMP set that are also not in the new CMP set. Other than Itga2b (which is a failed probe anyway), nothing screams. Also we'd have thrown Flt3 and Cd34 for in anyway because they're requisite cell surface markers (also Flt3 surface marker is expensive but otherwise not noteworthy and not used in the current sorting strategy)

What about cell surface marker expression?
* Cd34
* Cd16/32
* Cd9
* Cd41
* Cd48
* Sca1 (just throw that in for sh*&s and giggles)
```{r, fig.height = 15, fig.width=10}
surface.markers <- c("Cd34", "Fcgr3", "Fcgr2b", "Cd9", "Itga2b", "Cd48", "Ly6a")
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)
```
Save as png
```{r}
png(filename = "FeaturePlot_CMP_surfaceMarkers_clustIDfacet.png", height = 1600, width = 1600)
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)
dev.off()
```







# Compare @g hierarchcial clusteirng

Do clustering using biomark RNAs as input
```{r}
# Read in BiomarkRNAs
biomark.rnas <- read.table('/Users/heustonef/Desktop/10XGenomicsData/BiomarkRNAs.txt')
biomark.rnas <- biomark.rnas$V1
```

use biomark RNAs to define dimensional reduction
```{r}
cmp.object <- readRDS("CMP_raw.RDS")
cmp.object <- RunPCA(cmp.object, features = biomark.rnas, ndims.print = 1:5, , nfeatures.print = 5)
ElbowPlot(cmp.object, ndims = 50)
```


Now run the clustering
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)


```

find the clusters

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
```

Plot the umaps and cell cluster ids
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```
### Calculate anticipated number of cells you'll find in each biomark cluster
Get # cells in each cluster

```{r}
tot.cellcount <- nrow(cmp.object@meta.data)
res05.list <- sort(unique(cmp.object@meta.data$RNA_snn_res.0.5), decreasing = FALSE)
sapply(res05.list, 
			 function(x){
			 	print(
			 		paste(
			 			"cluster", x, "=", 
			 			nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,]), 
			 			"cells or", 
			 			round(nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,])/tot.cellcount*100, digits = 2), 
			 			"% of total"
			 		)
			 	)
			 }
			)
```

So we did the dimensional reduction based on the biomark RNAs, then did our UMAP nearest neighbor clustering.


In the biomark hierarchcial clustering analysis I assayed 167 cells. The smallest cluster I detected had 3 cells, or 1.8% of total, and this is an uncomfortably small number of cells. Based on the UMAP calculations I would therefore expect to find 11 or 12 of the predicted 15 clusters. I found 12, and I don't really like that last one, so 11 or 12. Since I did the hierarchcial clustering yesterday and did this math today, we can say it was independent of these results and therefore totally legit. Yay!!


>>>>>>> cmp